#!/usr/bin/env python3
"""Monty-backed API orchestration executor (v2).

v2 goals:
- HfApi-first helper implementations for endpoints covered by huggingface_hub.
- Thin raw API fallback for uncovered endpoints (/api/recent-activity, /api/trending,
  /api/users/<u>/likes event stream, collections q-search).
- Stable machine-first helper envelopes (`items` + optional `item`, no polymorphic payloads).
"""

from __future__ import annotations

import argparse
import asyncio
import ast
import inspect
import json
import os
import re
import time
from itertools import islice
from typing import Any, Callable, cast, get_args
from urllib.error import HTTPError, URLError
from urllib.parse import urlencode
from urllib.request import Request, urlopen

from huggingface_hub import HfApi
from huggingface_hub.hf_api import DatasetSort_T, ModelSort_T, SpaceSort_T

# Runtime-level execution limits.
# - max_calls: hard cap on the total number of external helper/API calls a single
#   generated program may make in one run.
# - timeout_sec: wall-clock timeout for the full Monty execution.
DEFAULT_TIMEOUT_SEC = 90  # Default end-to-end timeout for one Monty run.
DEFAULT_MAX_CALLS = 400  # Default external-call budget exposed to callers.
MAX_CALLS_LIMIT = 400  # Absolute max external-call budget accepted by the runtime.
INTERNAL_STRICT_MODE = False

# Result-size vocabulary used throughout helper metadata:
# - return_limit: how many rows the caller wants back from a helper.
# - scan_limit / max_pages: how much source data a helper is willing to inspect
#   internally to answer the query.
# - hard cap: an absolute runtime-imposed maximum on rows returned in one helper call.
OUTPUT_ITEMS_TRUNCATION_LIMIT = 500  # Final output truncation for oversized `items` payloads.
EXHAUSTIVE_HELPER_RETURN_HARD_CAP = 2_000  # Runtime hard cap for exhaustive-helper output rows.
SELECTIVE_ENDPOINT_RETURN_HARD_CAP = 200  # Default cap for one-shot selective endpoint helpers.
TRENDING_ENDPOINT_MAX_LIMIT = 20  # Upstream `/api/trending` endpoint maximum.

# Exhaustive helper scan/page ceilings. These bound how much upstream data we
# inspect, which is different from how many rows we return to the caller.
GRAPH_SCAN_LIMIT_CAP = 10_000  # Max follower/member rows scanned in one helper call.
LIKES_SCAN_LIMIT_CAP = 10_000  # Max like-event rows scanned in one helper call.
LIKES_RANKING_WINDOW_DEFAULT = 40  # Default shortlist size when ranking likes by repo popularity.
LIKES_ENRICHMENT_MAX_REPOS = 50  # Max liked repos enriched with extra repo-detail calls.
RECENT_ACTIVITY_PAGE_SIZE = 100  # Rows requested per `/api/recent-activity` page.
RECENT_ACTIVITY_SCAN_MAX_PAGES = 10  # Max recent-activity pages fetched in one helper call.

# Compact summary helpers intentionally inspect less data than the full
# exhaustive helpers so they remain fast and predictable.
USER_SUMMARY_GRAPH_SCAN_LIMIT = 1_000  # Follower/following rows sampled for user summary.
USER_SUMMARY_LIKES_SCAN_LIMIT = 1_000  # Like rows sampled for user summary.
USER_SUMMARY_ACTIVITY_MAX_PAGES = 3  # Activity pages sampled for user summary.

# Monty sandbox resource limits. These constrain the Python execution
# environment itself rather than Hub/API pagination behavior.
DEFAULT_MONTY_MAX_MEMORY = 64 * 1024 * 1024  # 64 MiB
DEFAULT_MONTY_MAX_ALLOCATIONS = 250_000  # Approximate object-allocation ceiling in the sandbox.
DEFAULT_MONTY_MAX_RECURSION_DEPTH = 100  # Python recursion limit inside the sandbox.

_MODEL_SORT_KEYS = set(get_args(ModelSort_T)) or {
    "created_at",
    "downloads",
    "last_modified",
    "likes",
    "trending_score",
}
_DATASET_SORT_KEYS = set(get_args(DatasetSort_T)) or {
    "created_at",
    "downloads",
    "last_modified",
    "likes",
    "trending_score",
}
_SPACE_SORT_KEYS = set(get_args(SpaceSort_T)) or {
    "created_at",
    "last_modified",
    "likes",
    "trending_score",
}

_REPO_SORT_KEYS: dict[str, set[str]] = {
    "model": _MODEL_SORT_KEYS,
    "dataset": _DATASET_SORT_KEYS,
    "space": _SPACE_SORT_KEYS,
}

_SORT_KEY_ALIASES: dict[str, str] = {
    "createdat": "created_at",
    "created_at": "created_at",
    "created-at": "created_at",
    "downloads": "downloads",
    "likes": "likes",
    "lastmodified": "last_modified",
    "last_modified": "last_modified",
    "last-modified": "last_modified",
    "trendingscore": "trending_score",
    "trending_score": "trending_score",
    "trending-score": "trending_score",
    "trending": "trending_score",
}

_USER_FIELD_ALIASES: dict[str, str] = {
    "login": "username",
    "user": "username",
    "handle": "username",
    "name": "fullname",
    "full_name": "fullname",
    "full-name": "fullname",
    "is_pro": "isPro",
    "ispro": "isPro",
    "pro": "isPro",
}

_ACTOR_FIELD_ALIASES: dict[str, str] = {
    **_USER_FIELD_ALIASES,
    "entity_type": "type",
    "user_type": "type",
    "actor_type": "type",
}

# Repo helpers prefer canonical snake_case field names in generated code, but
# tolerate common camelCase/raw endpoint aliases when callers project with
# `fields=[...]`.
_REPO_FIELD_ALIASES: dict[str, str] = {
    "repoid": "repo_id",
    "repotype": "repo_type",
    "repourl": "repo_url",
    "createdat": "created_at",
    "lastmodified": "last_modified",
    "pipelinetag": "pipeline_tag",
    "trendingscore": "trending_score",
    "libraryname": "library_name",
    "paperswithcodeid": "paperswithcode_id",
}

_COLLECTION_FIELD_ALIASES: dict[str, str] = {
    "collectionid": "collection_id",
    "lastupdated": "last_updated",
    "ownertype": "owner_type",
    "itemcount": "item_count",
    "author": "owner",
}

REPO_CANONICAL_FIELDS: tuple[str, ...] = (
    "repo_id",
    "repo_type",
    "title",
    "author",
    "likes",
    "downloads",
    "created_at",
    "last_modified",
    "pipeline_tag",
    "repo_url",
    "tags",
    "library_name",
    "description",
    "paperswithcode_id",
    "sdk",
    "models",
    "datasets",
    "subdomain",
)

USER_CANONICAL_FIELDS: tuple[str, ...] = (
    "username",
    "fullname",
    "bio",
    "websiteUrl",
    "twitter",
    "github",
    "linkedin",
    "bluesky",
    "followers",
    "following",
    "likes",
    "isPro",
)

PROFILE_CANONICAL_FIELDS: tuple[str, ...] = (
    "handle",
    "entity_type",
    "display_name",
    "bio",
    "description",
    "avatar_url",
    "website_url",
    "twitter_url",
    "github_url",
    "linkedin_url",
    "bluesky_url",
    "followers_count",
    "following_count",
    "likes_count",
    "members_count",
    "models_count",
    "datasets_count",
    "spaces_count",
    "discussions_count",
    "papers_count",
    "upvotes_count",
    "organizations",
    "is_pro",
    "likes_sample",
    "activity_sample",
)

ACTOR_CANONICAL_FIELDS: tuple[str, ...] = (
    "username",
    "fullname",
    "isPro",
    "role",
    "type",
)

ACTIVITY_CANONICAL_FIELDS: tuple[str, ...] = (
    "event_type",
    "repo_id",
    "repo_type",
    "timestamp",
)

COLLECTION_CANONICAL_FIELDS: tuple[str, ...] = (
    "collection_id",
    "slug",
    "title",
    "owner",
    "owner_type",
    "description",
    "last_updated",
    "item_count",
)

# Extra hf_repo_search kwargs intentionally supported as pass-through to
# huggingface_hub.HfApi.list_models/list_datasets/list_spaces.
# (Generic args like `query/search/sort/author/limit` are handled directly in
# hf_repo_search signature and are not listed here.)
_REPO_SEARCH_EXTRA_ARGS: dict[str, set[str]] = {
    "model": {
        "filter",
        "apps",
        "gated",
        "inference",
        "inference_provider",
        "model_name",
        "trained_dataset",
        "pipeline_tag",
        "emissions_thresholds",
        "expand",
        "full",
        "cardData",
        "card_data",  # alias
        "fetch_config",
    },
    "dataset": {
        "filter",
        "benchmark",
        "dataset_name",
        "gated",
        "language_creators",
        "language",
        "multilinguality",
        "size_categories",
        "task_categories",
        "task_ids",
        "expand",
        "full",
    },
    "space": {
        "filter",
        "datasets",
        "models",
        "linked",
        "expand",
        "full",
    },
}

# Rich default metadata for repo search. These raw endpoint expand keys are
# normalized into the stable repo-row field surface below; keep them aligned
# with `_build_repo_row(...)`, `_REPO_FIELD_ALIASES`, and the shared agent docs.
_REPO_SEARCH_DEFAULT_EXPAND: dict[str, list[str]] = {
    "model": [
        "author",
        "createdAt",
        "downloads",
        "gated",
        "lastModified",
        "library_name",
        "likes",
        "pipeline_tag",
        "private",
        "sha",
        "tags",
        "trendingScore",
    ],
    "dataset": [
        "author",
        "createdAt",
        "description",
        "downloads",
        "gated",
        "lastModified",
        "likes",
        "paperswithcode_id",
        "private",
        "sha",
        "tags",
        "trendingScore",
    ],
    "space": [
        "author",
        "createdAt",
        "datasets",
        "lastModified",
        "likes",
        "models",
        "private",
        "sdk",
        "sha",
        "subdomain",
        "tags",
        "trendingScore",
    ],
}

# Per-helper pagination defaults and ceilings.
# These values answer questions like:
# - "If the caller omits return_limit, how many rows should this helper return?"
# - "How much upstream data may this helper scan/page through internally?"
# - "What is the helper-specific max_return override, if any?"
PAGINATION_POLICY: dict[str, dict[str, Any]] = {
    "hf_user_graph": {
        "scan_max": GRAPH_SCAN_LIMIT_CAP,
        "default_return": 1_000,
        "max_return": GRAPH_SCAN_LIMIT_CAP,
    },
    "hf_org_members": {"scan_max": GRAPH_SCAN_LIMIT_CAP, "default_return": 1_000},
    "hf_repo_likers": {"default_return": 1_000},
    "hf_user_likes": {
        "scan_max": LIKES_SCAN_LIMIT_CAP,
        "default_return": 100,
        "ranking_default": LIKES_RANKING_WINDOW_DEFAULT,
        "enrich_max": LIKES_ENRICHMENT_MAX_REPOS,
    },
    "hf_recent_activity": {
        "page_limit": RECENT_ACTIVITY_PAGE_SIZE,
        "max_pages": RECENT_ACTIVITY_SCAN_MAX_PAGES,
        "default_return": 100,
    },
    "hf_repo_search": {"max_return": 5_000, "default_return": 20},
    "hf_trending": {"max_return": TRENDING_ENDPOINT_MAX_LIMIT, "default_return": 20},
    "hf_collections_search": {"max_return": OUTPUT_ITEMS_TRUNCATION_LIMIT, "default_return": 20},
    "hf_collection_items": {"max_return": OUTPUT_ITEMS_TRUNCATION_LIMIT, "default_return": 100},
}

# Single source of truth for the public helper surface exposed to generated
# Monty code. Keep runtime helper resolution derived from this tuple.
HELPER_EXTERNALS = (
    "hf_runtime_capabilities",
    "hf_whoami",
    "hf_profile_summary",
    "hf_org_members",
    "hf_repo_search",
    "hf_user_graph",
    "hf_repo_likers",
    "hf_user_likes",
    "hf_recent_activity",
    "hf_repo_discussions",
    "hf_repo_discussion_details",
    "hf_repo_details",
    "hf_trending",
    "hf_collections_search",
    "hf_collection_items",
)

HELPER_COVERED_ENDPOINT_PATTERNS: list[tuple[str, str]] = [
    (r"^/api/whoami-v2$", "hf_whoami"),
    (r"^/api/trending$", "hf_trending"),
    (r"^/api/recent-activity$", "hf_recent_activity"),
    (r"^/api/models$", "hf_repo_search"),
    (r"^/api/datasets$", "hf_repo_search"),
    (r"^/api/spaces$", "hf_repo_search"),
    (r"^/api/(models|datasets|spaces)/[^/]+/[^/]+$", "hf_repo_details"),
    (r"^/api/(models|datasets|spaces)/[^/]+/[^/]+/discussions$", "hf_repo_discussions"),
    (r"^/api/(models|datasets|spaces)/[^/]+/[^/]+/discussions/\d+$", "hf_repo_discussion_details"),
    (r"^/api/(models|datasets|spaces)/(?:[^/]+|[^/]+/[^/]+)/likers$", "hf_repo_likers"),
    (r"^/api/users/[^/]+/overview$", "hf_profile_summary"),
    (r"^/api/organizations/[^/]+/overview$", "hf_profile_summary"),
    (r"^/api/users/[^/]+/likes$", "hf_user_likes"),
    (r"^/api/users/[^/]+/(followers|following)$", "hf_user_graph"),
    (r"^/api/organizations/[^/]+/members$", "hf_org_members"),
    (r"^/api/organizations/[^/]+/followers$", "hf_user_graph"),
    (r"^/api/collections$", "hf_collections_search"),
    (r"^/api/collections/[^/]+$", "hf_collection_items"),
    (r"^/api/collections/[^/]+/[^/]+$", "hf_collection_items"),
]


def _resolve_helper_functions(namespace: dict[str, Any]) -> dict[str, Callable[..., Any]]:
    resolved: dict[str, Callable[..., Any]] = {}
    for helper_name in HELPER_EXTERNALS:
        candidate = namespace.get(helper_name)
        if not callable(candidate):
            raise RuntimeError(f"Helper '{helper_name}' is not defined or not callable")
        resolved[helper_name] = cast(Callable[..., Any], candidate)
    return resolved

ALLOWLIST_PATTERNS = [
    r"^/api/whoami-v2$",
    r"^/api/trending$",
    r"^/api/daily_papers$",
    r"^/api/models$",
    r"^/api/datasets$",
    r"^/api/spaces$",
    r"^/api/models-tags-by-type$",
    r"^/api/datasets-tags-by-type$",
    r"^/api/(models|datasets|spaces)/[^/]+/[^/]+$",
    r"^/api/(models|datasets|spaces)/[^/]+/[^/]+/discussions$",
    r"^/api/(models|datasets|spaces)/[^/]+/[^/]+/discussions/\d+$",
    r"^/api/(models|datasets|spaces)/[^/]+/[^/]+/discussions/\d+/status$",
    r"^/api/users/[^/]+/overview$",
    r"^/api/users/[^/]+/socials$",
    r"^/api/users/[^/]+/followers$",
    r"^/api/users/[^/]+/following$",
    r"^/api/users/[^/]+/likes$",
    r"^/api/(models|datasets|spaces)/(?:[^/]+|[^/]+/[^/]+)/likers$",
    r"^/api/organizations/[^/]+/overview$",
    r"^/api/organizations/[^/]+/members$",
    r"^/api/organizations/[^/]+/followers$",
    r"^/api/collections$",
    r"^/api/collections/[^/]+$",
    r"^/api/collections/[^/]+/[^/]+$",
    r"^/api/recent-activity$",
]

STRICT_ALLOWLIST_PATTERNS = [
    r"^/api/users/[^/]+/overview$",
    r"^/api/users/[^/]+/socials$",
    r"^/api/whoami-v2$",
    r"^/api/trending$",
    r"^/api/daily_papers$",
    r"^/api/(models|datasets|spaces)/(?:[^/]+|[^/]+/[^/]+)/likers$",
    r"^/api/collections$",
    r"^/api/collections/[^/]+$",
    r"^/api/collections/[^/]+/[^/]+$",
    r"^/api/(models|datasets|spaces)/[^/]+/[^/]+/discussions$",
    r"^/api/(models|datasets|spaces)/[^/]+/[^/]+/discussions/\d+$",
    r"^/api/(models|datasets|spaces)/[^/]+/[^/]+/discussions/\d+/status$",
]


class MontyExecutionError(RuntimeError):
    def __init__(self, message: str, api_calls: int, trace: list[dict[str, Any]]):
        super().__init__(message)
        self.api_calls = api_calls
        self.trace = trace


def _load_request_token() -> str | None:
    try:
        from fast_agent.mcp.auth.context import request_bearer_token  # type: ignore

        token = request_bearer_token.get()
        if token:
            return token
    except Exception:
        pass
    return None


def _load_token() -> str | None:
    token = _load_request_token()
    if token:
        return token
    return os.getenv("HF_TOKEN") or None


def _normalize_endpoint(endpoint: str) -> str:
    ep = (endpoint or "").strip()
    if not ep:
        raise ValueError("endpoint is required")
    if "?" in ep:
        raise ValueError("endpoint must not include query string; use params")
    if ep.startswith("http://") or ep.startswith("https://"):
        raise ValueError("endpoint must be path-only")
    if not ep.startswith("/"):
        ep = "/" + ep
    if not ep.startswith("/api/"):
        ep = "/api" + ep
    if ep in {"/api/collections/search", "/api/collections/search/"}:
        ep = "/api/collections"
    if ".." in ep:
        raise ValueError("path traversal not allowed")
    return ep


def _endpoint_allowed(endpoint: str, strict_mode: bool) -> bool:
    path = endpoint.split("?", 1)[0]
    patterns = STRICT_ALLOWLIST_PATTERNS if strict_mode else ALLOWLIST_PATTERNS
    return any(re.match(p, path) for p in patterns)


def _json_best_effort(raw: bytes) -> Any:
    try:
        return json.loads(raw)
    except Exception:
        return raw.decode("utf-8", errors="replace")


def _sanitize_params(endpoint: str, params: dict[str, Any] | None) -> dict[str, Any]:
    clean = dict(params or {})
    path = endpoint.split("?", 1)[0]

    if path == "/api/collections":
        if "q" not in clean and "search" in clean:
            clean["q"] = clean.get("search")
        clean.pop("search", None)

    if path == "/api/trending":
        t = str(clean.get("type") or "").strip().lower()
        aliases = {"models": "model", "datasets": "dataset", "spaces": "space"}
        if t in aliases:
            clean["type"] = aliases[t]
        lim = clean.get("limit")
        if lim is not None:
            try:
                n = int(lim)
            except Exception:
                n = TRENDING_ENDPOINT_MAX_LIMIT
            clean["limit"] = max(1, min(n, TRENDING_ENDPOINT_MAX_LIMIT))
        return clean

    lim = clean.get("limit")
    if lim is None:
        return clean
    try:
        n = int(lim)
    except Exception:
        return clean

    endpoint_limit_max = SELECTIVE_ENDPOINT_RETURN_HARD_CAP
    if re.match(r"^/api/users/[^/]+/(followers|following)$", path):
        endpoint_limit_max = GRAPH_SCAN_LIMIT_CAP
    elif re.match(r"^/api/users/[^/]+/likes$", path):
        endpoint_limit_max = LIKES_SCAN_LIMIT_CAP

    clean["limit"] = max(1, min(n, endpoint_limit_max))
    return clean


def _truncate_result_payload(output: Any) -> Any:
    if not isinstance(output, dict):
        return output

    items = output.get("items")
    if not isinstance(items, list) or len(items) <= OUTPUT_ITEMS_TRUNCATION_LIMIT:
        return output

    trimmed = dict(output)
    trimmed_items = items[:OUTPUT_ITEMS_TRUNCATION_LIMIT]
    trimmed["items"] = trimmed_items
    trimmed["item"] = trimmed_items[0] if len(trimmed_items) == 1 else None
    note = f"truncated items to first {OUTPUT_ITEMS_TRUNCATION_LIMIT} rows for token efficiency"
    steps = trimmed.get("steps")
    if isinstance(steps, list):
        trimmed["steps"] = [*steps, note]
    else:
        trimmed["steps"] = [note]
    return trimmed


def _is_helper_envelope(output: Any) -> bool:
    return (
        isinstance(output, dict)
        and isinstance(output.get("ok"), bool)
        and "items" in output
        and "meta" in output
        and "error" in output
    )


def _summarize_limit_hit(helper_name: str, result: Any) -> dict[str, Any] | None:
    if not _is_helper_envelope(result):
        return None
    meta = result.get("meta") if isinstance(result.get("meta"), dict) else {}
    if not isinstance(meta, dict):
        return None

    truncated_by = str(meta.get("truncated_by") or "")
    limit_hit = any(
        [
            meta.get("truncated") is True,
            meta.get("hard_cap_applied") is True,
            truncated_by in {"scan_limit", "page_limit", "multiple"},
        ]
    )
    if not limit_hit:
        return None

    summary: dict[str, Any] = {
        "helper": helper_name,
        "source": meta.get("source"),
        "returned": meta.get("returned"),
        "total": meta.get("total"),
        "truncated": meta.get("truncated"),
        "truncated_by": meta.get("truncated_by"),
        "more_available": meta.get("more_available"),
        "requested_return_limit": meta.get("requested_return_limit"),
        "applied_return_limit": meta.get("applied_return_limit"),
        "next_request_hint": meta.get("next_request_hint"),
    }
    if meta.get("scan_limit") is not None:
        summary["scan_limit"] = meta.get("scan_limit")
    if meta.get("applied_max_pages") is not None:
        summary["applied_max_pages"] = meta.get("applied_max_pages")
    return summary


def _wrap_raw_result(
    result: Any,
    *,
    ok: bool,
    api_calls: int,
    elapsed_ms: int,
    limit_summaries: list[dict[str, Any]] | None = None,
    error: str | None = None,
) -> dict[str, Any]:
    hits = [dict(summary) for summary in (limit_summaries or [])[:10]]
    meta: dict[str, Any] = {
        "ok": ok,
        "api_calls": api_calls,
        "elapsed_ms": elapsed_ms,
        "limits_reached": bool(hits),
        "limit_summary": hits,
    }
    if error is not None:
        meta["error"] = error
    return {
        "result": result,
        "meta": meta,
    }


def _clamp_int(value: Any, *, default: int, minimum: int, maximum: int) -> int:
    try:
        out = int(value)
    except Exception:
        out = default
    return max(minimum, min(out, maximum))


def _as_int(value: Any) -> int | None:
    try:
        return int(value)
    except Exception:
        return None


def _canonical_repo_type(value: Any, *, default: str = "model") -> str:
    raw = str(value or "").strip().lower()
    aliases = {
        "model": "model",
        "models": "model",
        "dataset": "dataset",
        "datasets": "dataset",
        "space": "space",
        "spaces": "space",
    }
    return aliases.get(raw, default)


def _normalize_repo_sort_key(repo_type: str, sort_value: Any) -> tuple[str | None, str | None]:
    raw = str(sort_value or "").strip()
    if not raw:
        return None, None

    key = _SORT_KEY_ALIASES.get(raw.lower().replace(" ", "").replace("__", "_"))
    if key is None:
        key = _SORT_KEY_ALIASES.get(raw.lower())
    if key is None:
        return None, f"Invalid sort key '{raw}'"

    rt = _canonical_repo_type(repo_type)
    allowed = _REPO_SORT_KEYS.get(rt, set())
    if key not in allowed:
        return None, f"Invalid sort key '{raw}' for repo_type='{rt}'. Allowed: {', '.join(sorted(allowed))}"
    return key, None


def _repo_detail_endpoint(repo_type: str, repo_id: str) -> str:
    rt = _canonical_repo_type(repo_type)
    rid = str(repo_id or "").strip()
    if "/" not in rid:
        raise ValueError("repo_id must be owner/name")
    owner, name = rid.split("/", 1)
    if not owner or not name:
        raise ValueError("repo_id must be owner/name")
    return f"/api/{rt}s/{owner}/{name}"


def _coerce_str_list(value: Any) -> list[str]:
    if value is None:
        return []
    if isinstance(value, str):
        raw = [value]
    elif isinstance(value, (list, tuple, set)):
        raw = list(value)
    else:
        raise ValueError("Expected a string or list of strings")
    return [str(v).strip() for v in raw if str(v).strip()]


def _optional_str_list(value: Any) -> list[str] | None:
    if value is None:
        return None
    if isinstance(value, str):
        out = [value.strip()] if value.strip() else []
        return out or None
    if isinstance(value, (list, tuple, set)):
        out = [str(v).strip() for v in value if str(v).strip()]
        return out or None
    return None


def _dt_to_str(value: Any) -> str | None:
    if value is None:
        return None
    iso = getattr(value, "isoformat", None)
    if callable(iso):
        try:
            return str(iso())
        except Exception:
            pass
    return str(value)


def _repo_web_url(repo_type: str, repo_id: str | None) -> str | None:
    if not isinstance(repo_id, str) or not repo_id:
        return None
    base = os.getenv("HF_ENDPOINT", "https://huggingface.co").rstrip("/")
    rt = _canonical_repo_type(repo_type, default="")
    if rt == "dataset":
        return f"{base}/datasets/{repo_id}"
    if rt == "space":
        return f"{base}/spaces/{repo_id}"
    return f"{base}/{repo_id}"


def _build_repo_row(
    *,
    repo_id: Any,
    repo_type: str,
    author: Any = None,
    title: Any = None,
    likes: Any = None,
    downloads: Any = None,
    created_at: Any = None,
    last_modified: Any = None,
    pipeline_tag: Any = None,
    private: Any = None,
    trending_score: Any = None,
    tags: Any = None,
    sha: Any = None,
    gated: Any = None,
    library_name: Any = None,
    description: Any = None,
    paperswithcode_id: Any = None,
    sdk: Any = None,
    models: Any = None,
    datasets: Any = None,
    subdomain: Any = None,
) -> dict[str, Any]:
    rt = _canonical_repo_type(repo_type)
    author_value = author
    if not isinstance(author_value, str) and isinstance(repo_id, str) and "/" in repo_id:
        author_value = repo_id.split("/", 1)[0]

    title_value = title
    if (not isinstance(title_value, str) or not title_value.strip()) and isinstance(repo_id, str) and repo_id:
        title_value = repo_id if rt == "space" else None

    return {
        "id": repo_id,
        "slug": repo_id,
        "repo_id": repo_id,
        "title": title_value,
        "repo_type": rt,
        "author": author_value,
        "likes": _as_int(likes),
        "downloads": _as_int(downloads),
        "created_at": _dt_to_str(created_at),
        "last_modified": _dt_to_str(last_modified),
        "pipeline_tag": pipeline_tag,
        "private": private,
        "trending_score": _as_int(trending_score) if trending_score is not None else None,
        "repo_url": _repo_web_url(rt, repo_id if isinstance(repo_id, str) else None),
        "tags": _optional_str_list(tags),
        "sha": sha,
        "gated": gated,
        "library_name": library_name,
        "description": description,
        "paperswithcode_id": paperswithcode_id,
        "sdk": sdk,
        "models": _optional_str_list(models),
        "datasets": _optional_str_list(datasets),
        "subdomain": subdomain,
    }


def _normalize_repo_search_row(row: Any, repo_type: str) -> dict[str, Any]:
    return _build_repo_row(
        repo_id=getattr(row, "id", None),
        repo_type=repo_type,
        author=getattr(row, "author", None),
        title=getattr(row, "title", None),
        likes=getattr(row, "likes", None),
        downloads=getattr(row, "downloads", None),
        created_at=getattr(row, "created_at", None),
        last_modified=getattr(row, "last_modified", None),
        pipeline_tag=getattr(row, "pipeline_tag", None),
        private=getattr(row, "private", None),
        trending_score=getattr(row, "trending_score", None),
        tags=getattr(row, "tags", None),
        sha=getattr(row, "sha", None),
        gated=getattr(row, "gated", None),
        library_name=getattr(row, "library_name", None),
        description=getattr(row, "description", None),
        paperswithcode_id=getattr(row, "paperswithcode_id", None),
        sdk=getattr(row, "sdk", None),
        models=getattr(row, "models", None),
        datasets=getattr(row, "datasets", None),
        subdomain=getattr(row, "subdomain", None),
    )


def _normalize_repo_detail_row(detail: Any, repo_type: str, repo_id: str) -> dict[str, Any]:
    row = _normalize_repo_search_row(detail, repo_type)
    resolved_repo_id = row.get("repo_id") or repo_id
    row["id"] = row.get("id") or resolved_repo_id
    row["slug"] = row.get("slug") or resolved_repo_id
    row["repo_id"] = resolved_repo_id
    row["repo_url"] = _repo_web_url(repo_type, resolved_repo_id)
    return row


def _normalize_trending_row(repo: dict[str, Any], default_repo_type: str, rank: int | None = None) -> dict[str, Any]:
    row = _build_repo_row(
        repo_id=repo.get("id"),
        repo_type=repo.get("type") or default_repo_type,
        author=repo.get("author"),
        title=repo.get("title"),
        likes=repo.get("likes"),
        downloads=repo.get("downloads"),
        created_at=repo.get("createdAt"),
        last_modified=repo.get("lastModified"),
        pipeline_tag=repo.get("pipeline_tag"),
        private=repo.get("private"),
        trending_score=repo.get("trendingScore"),
        tags=repo.get("tags"),
        sha=repo.get("sha"),
        gated=repo.get("gated"),
        library_name=repo.get("library_name"),
        description=repo.get("description"),
        paperswithcode_id=repo.get("paperswithcode_id"),
        sdk=repo.get("sdk"),
        models=repo.get("models"),
        datasets=repo.get("datasets"),
        subdomain=repo.get("subdomain"),
    )
    if rank is not None:
        row["trending_rank"] = rank
    return row


def _normalize_collection_repo_item(row: dict[str, Any]) -> dict[str, Any] | None:
    repo_id = row.get("id") or row.get("repoId") or row.get("repo_id")
    if not isinstance(repo_id, str) or not repo_id:
        return None

    repo_type = _canonical_repo_type(row.get("repoType") or row.get("repo_type") or row.get("type"), default="")
    if repo_type not in {"model", "dataset", "space"}:
        return None

    return _build_repo_row(
        repo_id=repo_id,
        repo_type=repo_type,
        author=row.get("author") or _author_from_any(row.get("authorData")),
        title=row.get("title"),
        likes=row.get("likes"),
        downloads=row.get("downloads"),
        created_at=row.get("createdAt") or row.get("created_at"),
        last_modified=row.get("lastModified") or row.get("last_modified"),
        pipeline_tag=row.get("pipeline_tag") or row.get("pipelineTag"),
        private=row.get("private"),
        tags=row.get("tags"),
        gated=row.get("gated"),
        library_name=row.get("library_name") or row.get("libraryName"),
        description=row.get("description"),
        paperswithcode_id=row.get("paperswithcode_id") or row.get("paperswithcodeId"),
        sdk=row.get("sdk"),
        models=row.get("models"),
        datasets=row.get("datasets"),
        subdomain=row.get("subdomain"),
    )


def _sort_repo_rows(rows: list[dict[str, Any]], sort_key: str | None) -> list[dict[str, Any]]:
    if not sort_key:
        return rows

    if sort_key in {"likes", "downloads", "trending_score"}:
        return sorted(rows, key=lambda row: _as_int(row.get(sort_key)) or -1, reverse=True)

    if sort_key in {"created_at", "last_modified"}:
        return sorted(rows, key=lambda row: str(row.get(sort_key) or ""), reverse=True)

    return rows


def call_api_host(
    endpoint: str,
    *,
    method: str = "GET",
    params: dict[str, Any] | None = None,
    json_body: dict[str, Any] | None = None,
    timeout_sec: int = DEFAULT_TIMEOUT_SEC,
    strict_mode: bool = False,
) -> dict[str, Any]:
    method_u = method.upper().strip()
    if method_u not in {"GET", "POST"}:
        raise ValueError("Only GET and POST are supported")

    ep = _normalize_endpoint(endpoint)
    if not _endpoint_allowed(ep, strict_mode):
        raise ValueError(f"Endpoint not allowed: {ep}")

    params = _sanitize_params(ep, params)
    if ep == "/api/recent-activity":
        feed_type = str((params or {}).get("feedType", "")).strip().lower()
        if feed_type not in {"user", "org"}:
            raise ValueError("/api/recent-activity requires feedType=user|org")
        if not str((params or {}).get("entity", "")).strip():
            raise ValueError("/api/recent-activity requires entity")

    base = os.getenv("HF_ENDPOINT", "https://huggingface.co").rstrip("/")
    q = urlencode(params or {}, doseq=True)
    url = f"{base}{ep}" + (f"?{q}" if q else "")

    headers = {"Accept": "application/json"}
    token = _load_token()
    if token:
        headers["Authorization"] = f"Bearer {token}"

    data = None
    if method_u == "POST":
        headers["Content-Type"] = "application/json"
        data = json.dumps(json_body or {}).encode("utf-8")

    req = Request(url, method=method_u, headers=headers, data=data)
    try:
        with urlopen(req, timeout=timeout_sec) as res:
            payload = _json_best_effort(res.read())
            return {"ok": True, "status": int(res.status), "url": url, "data": payload, "error": None}
    except HTTPError as e:
        payload = _json_best_effort(e.read())
        err = payload if isinstance(payload, str) else json.dumps(payload, ensure_ascii=False)[:1000]
        return {"ok": False, "status": int(e.code), "url": url, "data": payload, "error": err}
    except URLError as e:
        return {"ok": False, "status": 0, "url": url, "data": None, "error": f"Network error: {e}"}


def _validate_generated_code(code: str) -> None:
    if not code.strip():
        raise ValueError("Generated code is empty")

    blocked_patterns: list[tuple[str, str]] = [
        (r"(?m)^\s*import\s+\S", "import statement"),
        (r"(?m)^\s*from\s+\S+\s+import\s+\S", "from-import statement"),
        (r"\bexec\s*\(", "exec("),
        (r"\beval\s*\(", "eval("),
        (r"\bopen\s*\(", "open("),
        (r"\b__import__\b", "__import__"),
        (r"(?i)\bwhile\s+true\b", "while true"),
    ]
    for pattern, label in blocked_patterns:
        if re.search(pattern, code):
            raise ValueError(f"Generated code contains blocked pattern: {label}")

    try:
        parsed = compile(  # noqa: S102 - compile is used for AST validation only.
            code,
            "<generated-monty-code>",
            "exec",
            flags=ast.PyCF_ONLY_AST | ast.PyCF_ALLOW_TOP_LEVEL_AWAIT,
            dont_inherit=True,
        )
    except SyntaxError as e:
        message = e.msg or "invalid syntax"
        raise ValueError(f"Generated code is not valid Python: {message}") from e

    if not isinstance(parsed, ast.Module):
        raise ValueError("Generated code must be a Python module")

    solve_defs = [
        node
        for node in parsed.body
        if isinstance(node, ast.AsyncFunctionDef) and node.name == "solve"
    ]
    if not solve_defs:
        raise ValueError("Generated code must define `async def solve(query, max_calls): ...`.")

    def _valid_solve_signature(node: ast.AsyncFunctionDef) -> bool:
        args = node.args
        return (
            not args.posonlyargs
            and len(args.args) == 2
            and [arg.arg for arg in args.args] == ["query", "max_calls"]
            and args.vararg is None
            and not args.kwonlyargs
            and args.kwarg is None
            and not args.defaults
            and not args.kw_defaults
        )

    if not any(_valid_solve_signature(node) for node in solve_defs):
        raise ValueError("`solve` must have signature `async def solve(query, max_calls): ...`.")

    if not parsed.body:
        raise ValueError("Generated code is empty")

    final_stmt = parsed.body[-1]
    valid_final_await = (
        isinstance(final_stmt, ast.Expr)
        and isinstance(final_stmt.value, ast.Await)
        and isinstance(final_stmt.value.value, ast.Call)
        and isinstance(final_stmt.value.value.func, ast.Name)
        and final_stmt.value.value.func.id == "solve"
        and len(final_stmt.value.value.args) == 2
        and not final_stmt.value.value.keywords
        and all(isinstance(arg, ast.Name) for arg in final_stmt.value.value.args)
        and [cast(ast.Name, arg).id for arg in final_stmt.value.value.args] == ["query", "max_calls"]
    )
    if not valid_final_await:
        raise ValueError("Generated code must end with `await solve(query, max_calls)`.")

    def _preferred_helper_for_endpoint(endpoint: str) -> str | None:
        for pattern, helper_name in HELPER_COVERED_ENDPOINT_PATTERNS:
            if re.match(pattern, endpoint):
                return helper_name
        return None

    def _call_api_endpoint_hint(expr: ast.AST | None) -> str | None:
        if isinstance(expr, ast.Constant) and isinstance(expr.value, str):
            return expr.value
        if isinstance(expr, ast.JoinedStr):
            literal_parts = [
                value.value
                for value in expr.values
                if isinstance(value, ast.Constant) and isinstance(value.value, str)
            ]
            if literal_parts:
                return "".join(literal_parts)
        return None

    for node in ast.walk(parsed):
        if not isinstance(node, ast.Call):
            continue
        if not isinstance(node.func, ast.Name) or node.func.id != "call_api":
            continue

        endpoint_expr: ast.AST | None = node.args[0] if node.args else None
        for keyword in node.keywords:
            if keyword.arg == "endpoint":
                endpoint_expr = keyword.value
                break

        endpoint_hint = _call_api_endpoint_hint(endpoint_expr)
        if endpoint_hint and "/api/collections/" in endpoint_hint and "/items" in endpoint_hint:
            raise ValueError("Use `hf_collection_items(...)` for collection contents instead of guessing `/api/collections/.../items`.")
        if endpoint_hint:
            preferred_helper = _preferred_helper_for_endpoint(endpoint_hint)
            if preferred_helper is not None:
                raise ValueError(f"Use `{preferred_helper}(...)` instead of `call_api({endpoint_hint!r}, ...)` for this endpoint family.")

    allowed_external_calls = ["call_api(", *[f"{name}(" for name in HELPER_EXTERNALS]]
    if not any(token in code for token in allowed_external_calls):
        raise ValueError("Generated code must call at least one external API function (call_api or hf_* helper)")

    helper_name_set = set(HELPER_EXTERNALS)
    for m in re.finditer(r"call_api\(\s*([\"'])\s*([^\"']+)\s*\1", code):
        endpoint_literal = str(m.group(2) or "").strip()
        if not endpoint_literal:
            continue
        if (
            endpoint_literal in helper_name_set
            or endpoint_literal.startswith("hf_")
            or endpoint_literal.startswith("/hf_")
            or endpoint_literal.startswith("/api/hf_")
        ):
            raise ValueError("Do not call helper names through call_api; call hf_* helpers directly.")
        if re.match(r"^/api/collections/(?:[^/]+/)?[^/]+/items$", endpoint_literal):
            raise ValueError("Use `hf_collection_items(...)` for collection contents instead of guessing `/api/collections/.../items`.")
        preferred_helper = _preferred_helper_for_endpoint(endpoint_literal)
        if preferred_helper is not None:
            raise ValueError(f"Use `{preferred_helper}(...)` instead of `call_api({endpoint_literal!r}, ...)` for this endpoint family.")
        if not endpoint_literal.startswith("/api/"):
            raise ValueError("call_api endpoint must be a raw path starting with '/api/...'.")
async def _run_with_monty(
    *,
    code: str,
    query: str,
    max_calls: int,
    strict_mode: bool,
    timeout_sec: int,
) -> dict[str, Any]:
    try:
        import pydantic_monty
    except Exception as e:
        raise RuntimeError("pydantic_monty is not installed. Install with `uv pip install pydantic-monty`.") from e

    max_calls = max(1, min(int(max_calls), MAX_CALLS_LIMIT))
    call_count = {"n": 0}
    trace: list[dict[str, Any]] = []
    limit_summaries: list[dict[str, Any]] = []
    latest_helper_error: dict[str, Any] | None = None
    internal_helper_used = {"used": False}
    def _budget_remaining() -> int:
        return max(0, max_calls - call_count["n"])

    def _policy_int(helper_name: str, key: str, default: int) -> int:
        cfg = PAGINATION_POLICY.get(helper_name) or {}
        try:
            return int(cfg.get(key, default))
        except Exception:
            return int(default)

    def _consume_call(endpoint: str, method: str = "GET") -> int:
        if call_count["n"] >= max_calls:
            raise RuntimeError(f"Max API calls exceeded ({max_calls})")
        call_count["n"] += 1
        return call_count["n"]

    def _trace_ok(idx: int, endpoint: str, method: str = "GET", status: int = 200) -> None:
        trace.append(
            {
                "call_index": idx,
                "depth": idx,
                "method": method,
                "endpoint": endpoint,
                "ok": True,
                "status": status,
            }
        )

    def _trace_err(idx: int, endpoint: str, err: Any, method: str = "GET", status: int = 0) -> None:
        trace.append(
            {
                "call_index": idx,
                "depth": idx,
                "method": method,
                "endpoint": endpoint,
                "ok": False,
                "status": status,
                "error": str(err),
            }
        )

    def _host_raw_call(
        endpoint: str,
        *,
        params: dict[str, Any] | None = None,
        method: str = "GET",
        json_body: dict[str, Any] | None = None,
    ) -> dict[str, Any]:
        idx = _consume_call(endpoint, method)
        try:
            resp = call_api_host(
                endpoint,
                method=method,
                params=params,
                json_body=json_body,
                timeout_sec=timeout_sec,
                strict_mode=strict_mode,
            )
            if resp.get("ok"):
                _trace_ok(idx, endpoint, method=method, status=int(resp.get("status") or 200))
            else:
                _trace_err(idx, endpoint, resp.get("error"), method=method, status=int(resp.get("status") or 0))
            return resp
        except Exception as e:
            _trace_err(idx, endpoint, e, method=method, status=0)
            raise

    hf_api_client: HfApi | None = None

    def _get_hf_api_client() -> HfApi:
        nonlocal hf_api_client
        if hf_api_client is None:
            endpoint = os.getenv("HF_ENDPOINT", "https://huggingface.co").rstrip("/")
            hf_api_client = HfApi(endpoint=endpoint, token=_load_token())
        return hf_api_client

    def _host_hf_call(endpoint: str, fn: Callable[[], Any]) -> Any:
        idx = _consume_call(endpoint, "GET")
        try:
            out = fn()
            _trace_ok(idx, endpoint, method="GET", status=200)
            return out
        except Exception as e:
            _trace_err(idx, endpoint, e, method="GET", status=0)
            raise

    def _helper_meta(start_calls: int, *, source: str, **extra: Any) -> dict[str, Any]:
        out = {
            "source": source,
            "normalized": True,
            "budget_used": max(0, call_count["n"] - start_calls),
            "budget_remaining": _budget_remaining(),
        }
        out.update(extra)
        return out

    def _derive_limit_metadata(
        *,
        requested_return_limit: int | None,
        applied_return_limit: int,
        default_limit_used: bool,
        requested_scan_limit: int | None = None,
        applied_scan_limit: int | None = None,
        requested_max_pages: int | None = None,
        applied_max_pages: int | None = None,
    ) -> dict[str, Any]:
        meta: dict[str, Any] = {
            "requested_return_limit": requested_return_limit,
            "applied_return_limit": applied_return_limit,
            "default_limit_used": default_limit_used,
        }
        if requested_scan_limit is not None or applied_scan_limit is not None:
            meta["requested_scan_limit"] = requested_scan_limit
            meta["scan_limit"] = applied_scan_limit
            meta["scan_limit_applied"] = requested_scan_limit != applied_scan_limit
        if requested_max_pages is not None or applied_max_pages is not None:
            meta["requested_max_pages"] = requested_max_pages
            meta["applied_max_pages"] = applied_max_pages
            meta["page_limit_applied"] = requested_max_pages != applied_max_pages
        if requested_return_limit is not None:
            meta["hard_cap_applied"] = applied_return_limit < requested_return_limit
        return meta

    def _derive_more_available(*, sample_complete: bool, exact_count: bool, returned: int, total: int | None) -> bool | str:
        if sample_complete:
            return False
        if exact_count and total is not None and returned < total:
            return True
        return "unknown"

    def _derive_truncated_by(
        *,
        hard_cap: bool = False,
        scan_limit_hit: bool = False,
        page_limit_hit: bool = False,
        return_limit_hit: bool = False,
    ) -> str:
        causes = [hard_cap, scan_limit_hit, page_limit_hit, return_limit_hit]
        if sum(1 for cause in causes if cause) > 1:
            return "multiple"
        if hard_cap:
            return "hard_cap"
        if scan_limit_hit:
            return "scan_limit"
        if page_limit_hit:
            return "page_limit"
        if return_limit_hit:
            return "return_limit"
        return "none"

    def _derive_can_request_more(*, sample_complete: bool, truncated_by: str) -> bool:
        if sample_complete:
            return False
        return truncated_by in {"return_limit", "scan_limit", "page_limit", "multiple"}

    def _derive_next_request_hint(*, truncated_by: str, more_available: bool | str, applied_return_limit: int, applied_scan_limit: int | None = None, applied_max_pages: int | None = None) -> str:
        if truncated_by == "return_limit":
            return f"Ask for return_limit>{applied_return_limit} to see more rows"
        if truncated_by == "scan_limit" and applied_scan_limit is not None:
            return f"Increase scan_limit above {applied_scan_limit} for broader coverage"
        if truncated_by == "page_limit" and applied_max_pages is not None:
            return f"Increase max_pages above {applied_max_pages} to continue paging"
        if truncated_by == "hard_cap":
            return "No more rows can be returned in a single call because a hard cap was applied"
        if truncated_by == "multiple":
            return "Increase the relevant return/page/scan bounds to improve coverage"
        if more_available is False:
            return "No more results available"
        if more_available == "unknown":
            return "More results may exist; narrow filters or raise scan/page bounds for better coverage"
        return "Ask for a larger limit to see more rows"

    def _resolve_exhaustive_limits(
        *,
        return_limit: int | None,
        count_only: bool,
        default_return: int,
        max_return: int,
        scan_limit: int | None = None,
        scan_cap: int | None = None,
    ) -> dict[str, Any]:
        requested_return_limit = None if count_only else return_limit
        effective_requested_return_limit = 0 if count_only else requested_return_limit
        out: dict[str, Any] = {
            "requested_return_limit": requested_return_limit,
            "applied_return_limit": _clamp_int(
                effective_requested_return_limit,
                default=default_return,
                minimum=0,
                maximum=max_return,
            ),
            "default_limit_used": requested_return_limit is None and not count_only,
        }
        out["hard_cap_applied"] = (
            requested_return_limit is not None and out["applied_return_limit"] < requested_return_limit
        )
        if scan_cap is not None:
            out["requested_scan_limit"] = scan_limit
            out["applied_scan_limit"] = _clamp_int(
                scan_limit,
                default=scan_cap,
                minimum=1,
                maximum=scan_cap,
            )
        return out

    def _build_exhaustive_meta(
        *,
        base_meta: dict[str, Any],
        limit_plan: dict[str, Any],
        sample_complete: bool,
        exact_count: bool,
        truncated_by: str,
        more_available: bool | str,
        requested_max_pages: int | None = None,
        applied_max_pages: int | None = None,
    ) -> dict[str, Any]:
        meta = dict(base_meta)
        applied_return_limit = int(limit_plan["applied_return_limit"])
        applied_scan_limit = limit_plan.get("applied_scan_limit")
        meta.update(
            {
                "complete": sample_complete,
                "exact_count": exact_count,
                "sample_complete": sample_complete,
                "more_available": more_available,
                "can_request_more": _derive_can_request_more(
                    sample_complete=sample_complete,
                    truncated_by=truncated_by,
                ),
                "truncated_by": truncated_by,
                "next_request_hint": _derive_next_request_hint(
                    truncated_by=truncated_by,
                    more_available=more_available,
                    applied_return_limit=applied_return_limit,
                    applied_scan_limit=applied_scan_limit if isinstance(applied_scan_limit, int) else None,
                    applied_max_pages=applied_max_pages,
                ),
            }
        )
        meta.update(
            _derive_limit_metadata(
                requested_return_limit=limit_plan["requested_return_limit"],
                applied_return_limit=applied_return_limit,
                default_limit_used=bool(limit_plan["default_limit_used"]),
                requested_scan_limit=limit_plan.get("requested_scan_limit"),
                applied_scan_limit=applied_scan_limit if isinstance(applied_scan_limit, int) else None,
                requested_max_pages=requested_max_pages,
                applied_max_pages=applied_max_pages,
            )
        )
        return meta

    def _overview_count_only_success(
        *,
        start_calls: int,
        source: str,
        total: int,
        limit_plan: dict[str, Any],
        base_meta: dict[str, Any],
    ) -> dict[str, Any]:
        sample_complete = True
        more_available = False
        truncated_by = "none"
        meta = _build_exhaustive_meta(
            base_meta={
                **base_meta,
                "matched": total,
                "returned": 0,
                "total": total,
                "total_available": total,
                "total_matched": total,
                "truncated": False,
            },
            limit_plan=limit_plan,
            sample_complete=sample_complete,
            exact_count=True,
            truncated_by=truncated_by,
            more_available=more_available,
        )
        return _helper_success(
            start_calls=start_calls,
            source=source,
            items=[],
            meta=meta,
        )

    def _build_exhaustive_result_meta(
        *,
        base_meta: dict[str, Any],
        limit_plan: dict[str, Any],
        matched_count: int,
        returned_count: int,
        exact_count: bool,
        count_only: bool = False,
        sample_complete: bool | None = None,
        more_available: bool | str | None = None,
        scan_limit_hit: bool = False,
        page_limit_hit: bool = False,
        truncated_extra: bool = False,
        requested_max_pages: int | None = None,
        applied_max_pages: int | None = None,
    ) -> dict[str, Any]:
        applied_return_limit = int(limit_plan["applied_return_limit"])
        if count_only:
            effective_sample_complete = exact_count
        else:
            effective_sample_complete = (
                sample_complete
                if isinstance(sample_complete, bool)
                else exact_count and matched_count <= applied_return_limit
            )
        return_limit_hit = False if count_only else (applied_return_limit > 0 and matched_count > applied_return_limit)
        truncated_by = _derive_truncated_by(
            hard_cap=bool(limit_plan.get("hard_cap_applied")),
            scan_limit_hit=scan_limit_hit,
            page_limit_hit=page_limit_hit,
            return_limit_hit=return_limit_hit,
        )
        truncated = truncated_by != "none" or truncated_extra
        total_value = _as_int(base_meta.get("total"))
        effective_more_available = more_available
        if count_only and exact_count:
            effective_more_available = False
        if effective_more_available is None:
            effective_more_available = _derive_more_available(
                sample_complete=effective_sample_complete,
                exact_count=exact_count,
                returned=returned_count,
                total=total_value,
            )

        return _build_exhaustive_meta(
            base_meta={
                **base_meta,
                "matched": matched_count,
                "returned": returned_count,
                "truncated": truncated,
            },
            limit_plan=limit_plan,
            sample_complete=effective_sample_complete,
            exact_count=exact_count,
            truncated_by=truncated_by,
            more_available=effective_more_available,
            requested_max_pages=requested_max_pages,
            applied_max_pages=applied_max_pages,
        )

    def _helper_success(
        *,
        start_calls: int,
        source: str,
        items: list[dict[str, Any]],
        cursor: str | None = None,
        meta: dict[str, Any] | None = None,
        **extra_meta: Any,
    ) -> dict[str, Any]:
        merged_meta = dict(meta or {})
        merged_meta.update(extra_meta)
        if cursor is not None:
            merged_meta["cursor"] = cursor

        return {
            "ok": True,
            "item": items[0] if len(items) == 1 else None,
            "items": items,
            "meta": _helper_meta(start_calls, source=source, **merged_meta),
            "error": None,
        }

    def _helper_error(*, start_calls: int, source: str, error: Any, **meta: Any) -> dict[str, Any]:
        nonlocal latest_helper_error
        envelope = {
            "ok": False,
            "item": None,
            "items": [],
            "meta": _helper_meta(start_calls, source=source, **meta),
            "error": str(error),
        }
        latest_helper_error = envelope
        return envelope

    def _project_items(
        items: list[dict[str, Any]],
        fields: list[str] | None,
        aliases: dict[str, str] | None = None,
    ) -> list[dict[str, Any]]:
        if not isinstance(fields, list) or not fields:
            return items
        wanted = [str(f).strip() for f in fields if str(f).strip()]
        if not wanted:
            return items
        alias_map = {str(k).strip().lower(): str(v).strip() for k, v in (aliases or {}).items() if str(k).strip() and str(v).strip()}
        projected: list[dict[str, Any]] = []
        for row in items:
            out: dict[str, Any] = {}
            for key in wanted:
                source_key = alias_map.get(key.lower(), key)
                value = row.get(source_key)
                if value is None:
                    continue
                out[key] = value
            projected.append(out)
        return projected

    def _project_repo_items(items: list[dict[str, Any]], fields: list[str] | None) -> list[dict[str, Any]]:
        return _project_items(items, fields, aliases=_REPO_FIELD_ALIASES)

    def _project_collection_items(items: list[dict[str, Any]], fields: list[str] | None) -> list[dict[str, Any]]:
        return _project_items(items, fields, aliases=_COLLECTION_FIELD_ALIASES)

    def _project_user_items(items: list[dict[str, Any]], fields: list[str] | None) -> list[dict[str, Any]]:
        return _project_items(items, fields, aliases=_USER_FIELD_ALIASES)

    def _project_actor_items(items: list[dict[str, Any]], fields: list[str] | None) -> list[dict[str, Any]]:
        return _project_items(items, fields, aliases=_ACTOR_FIELD_ALIASES)

    def _item_matches_where(item: dict[str, Any], where: dict[str, Any] | None) -> bool:
        if not isinstance(where, dict) or not where:
            return True
        for key, cond in where.items():
            val = item.get(str(key))
            if isinstance(cond, dict):
                if "eq" in cond and val != cond.get("eq"):
                    return False
                if "in" in cond:
                    allowed = cond.get("in")
                    if isinstance(allowed, (list, tuple, set)) and val not in allowed:
                        return False
                if "contains" in cond:
                    needle = cond.get("contains")
                    if not isinstance(val, str) or not isinstance(needle, str) or needle not in val:
                        return False
                if "icontains" in cond:
                    needle = cond.get("icontains")
                    if not isinstance(val, str) or not isinstance(needle, str) or needle.lower() not in val.lower():
                        return False
                if "gte" in cond:
                    v = _as_int(val)
                    c = _as_int(cond.get("gte"))
                    if v is None or c is None or v < c:
                        return False
                if "lte" in cond:
                    v = _as_int(val)
                    c = _as_int(cond.get("lte"))
                    if v is None or c is None or v > c:
                        return False
                continue

            if isinstance(cond, (list, tuple, set)):
                if val not in cond:
                    return False
                continue

            if val != cond:
                return False
        return True

    def _apply_where(items: list[dict[str, Any]], where: dict[str, Any] | None) -> list[dict[str, Any]]:
        if not isinstance(where, dict) or not where:
            return items
        return [row for row in items if _item_matches_where(row, where)]

    def _helper_item(resp: dict[str, Any]) -> dict[str, Any] | None:
        item = resp.get("item")
        if isinstance(item, dict):
            return item
        items = resp.get("items")
        if isinstance(items, list) and items and isinstance(items[0], dict):
            return items[0]
        return None

    def _overview_count(item: dict[str, Any] | None, key: str) -> int | None:
        if not isinstance(item, dict):
            return None
        return _as_int(item.get(key))

    def _summary_section(
        resp: dict[str, Any],
        *,
        count: int | None = None,
        default_sample: list[dict[str, Any]] | None = None,
    ) -> dict[str, Any]:
        meta = resp.get("meta")
        section_meta = dict(meta) if isinstance(meta, dict) else {}
        sample = resp.get("items")
        section_sample = sample if isinstance(sample, list) else list(default_sample or [])
        section_count = count
        if section_count is None:
            count_exact = section_meta.get("exact_count") is True or section_meta.get("count_source") in {"overview", "endpoint"}
            if count_exact:
                for key in ("total", "total_matched", "matched"):
                    section_count = _as_int(section_meta.get(key))
                    if section_count is not None:
                        break
        if resp.get("ok") is not True:
            section_meta["error"] = str(resp.get("error") or "section fetch failed")
            section_sample = list(default_sample or [])
        return {"count": section_count, "sample": section_sample, "meta": section_meta}

    async def _resolve_username_or_current(username: str | None) -> tuple[str | None, str | None]:
        u = str(username or "").strip()
        if u:
            return u, None

        whoami = await hf_whoami()
        if whoami.get("ok") is not True:
            return None, str(whoami.get("error") or "Could not resolve current authenticated user")

        item = _helper_item(whoami)
        resolved = item.get("username") if isinstance(item, dict) else None
        if not isinstance(resolved, str) or not resolved.strip():
            return None, "username was not provided and current authenticated user could not be resolved"
        return resolved.strip(), None

    def _normalize_user_likes_sort(sort: str | None) -> tuple[str | None, str | None]:
        raw = str(sort or "likedAt").strip()
        alias_map = {
            "": "likedAt",
            "likedat": "likedAt",
            "liked_at": "likedAt",
            "liked-at": "likedAt",
            "recency": "likedAt",
            "repolikes": "repoLikes",
            "repo_likes": "repoLikes",
            "repo-likes": "repoLikes",
            "repodownloads": "repoDownloads",
            "repo_downloads": "repoDownloads",
            "repo-downloads": "repoDownloads",
        }
        normalized = alias_map.get(raw.lower(), raw)
        if normalized not in {"likedAt", "repoLikes", "repoDownloads"}:
            return None, "sort must be one of likedAt, repoLikes, repoDownloads"
        return normalized, None

    def _author_from_any(value: Any) -> str | None:
        if isinstance(value, str):
            return value
        if isinstance(value, dict):
            for k in ("name", "username", "user", "login"):
                v = value.get(k)
                if isinstance(v, str) and v:
                    return v
        return None

    def _clean_social_handle(value: Any) -> str | None:
        if not isinstance(value, str):
            return None
        cleaned = value.strip()
        if not cleaned:
            return None
        if re.match(r"^https?://", cleaned, flags=re.IGNORECASE):
            return cleaned
        return cleaned.lstrip("@")

    def _social_url(kind: str, value: Any) -> str | None:
        cleaned = _clean_social_handle(value)
        if cleaned is None:
            return None
        if re.match(r"^https?://", cleaned, flags=re.IGNORECASE):
            return cleaned
        if kind == "twitter":
            return f"https://twitter.com/{cleaned}"
        if kind == "github":
            return f"https://github.com/{cleaned}"
        if kind == "linkedin":
            if cleaned.startswith(("in/", "company/")):
                return f"https://www.linkedin.com/{cleaned}"
            return f"https://www.linkedin.com/in/{cleaned}"
        if kind == "bluesky":
            return f"https://bsky.app/profile/{cleaned}"
        return cleaned

    async def call_api(
        endpoint: str,
        params: dict[str, Any] | None = None,
        method: str = "GET",
        json_body: dict[str, Any] | None = None,
    ) -> dict[str, Any]:
        return _host_raw_call(endpoint, params=params, method=method, json_body=json_body)

    async def hf_whoami() -> dict[str, Any]:
        start_calls = call_count["n"]
        endpoint = "/api/whoami-v2"
        token = _load_token()
        if token is None:
            return _helper_error(
                start_calls=start_calls,
                source=endpoint,
                error=(
                    "Current authenticated user is unavailable for this request. "
                    "No request-scoped or fallback HF token was found."
                ),
            )
        try:
            payload = _host_hf_call(
                endpoint,
                lambda: _get_hf_api_client().whoami(token=token, cache=True),
            )
        except Exception as e:
            return _helper_error(start_calls=start_calls, source=endpoint, error=e)

        username = payload.get("name") or payload.get("user") or payload.get("username")
        item = {"username": username, "fullname": payload.get("fullname"), "isPro": payload.get("isPro")}
        items = [item] if isinstance(username, str) and username else []
        return _helper_success(
            start_calls=start_calls,
            source=endpoint,
            items=items,
            scanned=1,
            matched=len(items),
            returned=len(items),
            truncated=False,
        )

    async def hf_user_overview(username: str) -> dict[str, Any]:
        start_calls = call_count["n"]
        u = str(username or "").strip()
        if not u:
            return _helper_error(start_calls=start_calls, source="/api/users/<u>/overview", error="username is required")
        endpoint = f"/api/users/{u}/overview"
        try:
            obj = _host_hf_call(endpoint, lambda: _get_hf_api_client().get_user_overview(u))
        except Exception as e:
            return _helper_error(start_calls=start_calls, source=endpoint, error=e)

        twitter = getattr(obj, "twitter", None) or getattr(obj, "twitterUsername", None)
        github = getattr(obj, "github", None) or getattr(obj, "githubUsername", None)
        linkedin = getattr(obj, "linkedin", None) or getattr(obj, "linkedinUsername", None)
        bluesky = getattr(obj, "bluesky", None) or getattr(obj, "blueskyUsername", None)

        if _budget_remaining() > 0 and any(v in {None, ""} for v in [twitter, github, linkedin, bluesky]):
            socials_ep = f"/api/users/{u}/socials"
            socials_resp = _host_raw_call(socials_ep)
            if socials_resp.get("ok"):
                socials_payload = socials_resp.get("data") if isinstance(socials_resp.get("data"), dict) else {}
                handles = socials_payload.get("socialHandles") if isinstance(socials_payload.get("socialHandles"), dict) else {}
                twitter = twitter or handles.get("twitter")
                github = github or handles.get("github")
                linkedin = linkedin or handles.get("linkedin")
                bluesky = bluesky or handles.get("bluesky")

        orgs_raw = getattr(obj, "orgs", None)
        org_names: list[str] | None = None
        if isinstance(orgs_raw, (list, tuple, set)):
            names = []
            for org in orgs_raw:
                if isinstance(org, str) and org.strip():
                    names.append(org.strip())
                    continue
                name = getattr(org, "name", None)
                if isinstance(name, str) and name.strip():
                    names.append(name.strip())
            org_names = names or None

        twitter_handle = _clean_social_handle(twitter)
        github_handle = _clean_social_handle(github)
        linkedin_handle = _clean_social_handle(linkedin)
        bluesky_handle = _clean_social_handle(bluesky)

        item = {
            "username": obj.username or u,
            "fullname": obj.fullname,
            "bio": getattr(obj, "details", None),
            "avatarUrl": obj.avatar_url,
            "websiteUrl": getattr(obj, "websiteUrl", None),
            "twitter": _social_url("twitter", twitter_handle),
            "github": _social_url("github", github_handle),
            "linkedin": _social_url("linkedin", linkedin_handle),
            "bluesky": _social_url("bluesky", bluesky_handle),
            "twitterHandle": twitter_handle,
            "githubHandle": github_handle,
            "linkedinHandle": linkedin_handle,
            "blueskyHandle": bluesky_handle,
            "followers": _as_int(obj.num_followers),
            "following": _as_int(obj.num_following),
            "likes": _as_int(obj.num_likes),
            "models": _as_int(getattr(obj, "num_models", None)),
            "datasets": _as_int(getattr(obj, "num_datasets", None)),
            "spaces": _as_int(getattr(obj, "num_spaces", None)),
            "discussions": _as_int(getattr(obj, "num_discussions", None)),
            "papers": _as_int(getattr(obj, "num_papers", None)),
            "upvotes": _as_int(getattr(obj, "num_upvotes", None)),
            "orgs": org_names,
            "isPro": obj.is_pro,
        }
        return _helper_success(
            start_calls=start_calls,
            source=endpoint,
            items=[item],
            scanned=1,
            matched=1,
            returned=1,
            truncated=False,
        )

    async def hf_org_overview(organization: str) -> dict[str, Any]:
        start_calls = call_count["n"]
        org = str(organization or "").strip()
        if not org:
            return _helper_error(
                start_calls=start_calls,
                source="/api/organizations/<o>/overview",
                error="organization is required",
            )
        endpoint = f"/api/organizations/{org}/overview"
        try:
            obj = _host_hf_call(endpoint, lambda: _get_hf_api_client().get_organization_overview(org))
        except Exception as e:
            return _helper_error(start_calls=start_calls, source=endpoint, error=e)

        item = {
            "organization": obj.name or org,
            "displayName": obj.fullname,
            "avatarUrl": obj.avatar_url,
            "description": obj.details,
            "websiteUrl": getattr(obj, "websiteUrl", None),
            "followers": _as_int(obj.num_followers),
            "members": _as_int(obj.num_users),
            "models": _as_int(getattr(obj, "num_models", None)),
            "datasets": _as_int(getattr(obj, "num_datasets", None)),
            "spaces": _as_int(getattr(obj, "num_spaces", None)),
        }
        return _helper_success(
            start_calls=start_calls,
            source=endpoint,
            items=[item],
            scanned=1,
            matched=1,
            returned=1,
            truncated=False,
        )

    async def hf_org_members(
        organization: str,
        return_limit: int | None = None,
        scan_limit: int | None = None,
        count_only: bool = False,
        where: dict[str, Any] | None = None,
        fields: list[str] | None = None,
    ) -> dict[str, Any]:
        start_calls = call_count["n"]
        org = str(organization or "").strip()
        if not org:
            return _helper_error(start_calls=start_calls, source="/api/organizations/<o>/members", error="organization is required")

        default_return = _policy_int("hf_org_members", "default_return", 100)
        scan_cap = _policy_int("hf_org_members", "scan_max", GRAPH_SCAN_LIMIT_CAP)
        limit_plan = _resolve_exhaustive_limits(
            return_limit=return_limit,
            count_only=count_only,
            default_return=default_return,
            max_return=EXHAUSTIVE_HELPER_RETURN_HARD_CAP,
            scan_limit=scan_limit,
            scan_cap=scan_cap,
        )
        ret_lim = int(limit_plan["applied_return_limit"])
        scan_lim = int(limit_plan["applied_scan_limit"])
        has_where = isinstance(where, dict) and bool(where)

        overview_total: int | None = None
        overview_source = f"/api/organizations/{org}/overview"
        if _budget_remaining() > 0:
            try:
                org_obj = _host_hf_call(overview_source, lambda: _get_hf_api_client().get_organization_overview(org))
                overview_total = _as_int(getattr(org_obj, "num_users", None))
            except Exception:
                overview_total = None

        if count_only and not has_where and overview_total is not None:
            return _overview_count_only_success(
                start_calls=start_calls,
                source=overview_source,
                total=overview_total,
                limit_plan=limit_plan,
                base_meta={
                    "scanned": 1,
                    "count_source": "overview",
                    "organization": org,
                },
            )

        endpoint = f"/api/organizations/{org}/members"
        try:
            rows = _host_hf_call(endpoint, lambda: list(islice(_get_hf_api_client().list_organization_members(org), scan_lim)))
        except Exception as e:
            return _helper_error(start_calls=start_calls, source=endpoint, error=e, organization=org)

        normalized: list[dict[str, Any]] = []
        for row in rows:
            handle = getattr(row, "username", None)
            if not isinstance(handle, str) or not handle:
                continue
            item = {
                "username": handle,
                "fullname": getattr(row, "fullname", None),
                "isPro": getattr(row, "is_pro", None),
                "role": getattr(row, "role", None),
            }
            normalized.append(item)

        normalized = _apply_where(normalized, where)
        observed_total = len(rows)
        scan_exhaustive = observed_total < scan_lim

        overview_list_mismatch = (
            overview_total is not None
            and scan_exhaustive
            and observed_total != overview_total
        )

        if has_where:
            exact_count = scan_exhaustive
            total = len(normalized)
            total_matched = len(normalized)
        else:
            if overview_total is not None:
                exact_count = True
                total = overview_total
                total_matched = overview_total
            else:
                exact_count = scan_exhaustive
                total = observed_total
                total_matched = observed_total

        total_available = overview_total if overview_total is not None else observed_total
        items = normalized[:ret_lim]
        scan_limit_hit = not exact_count and observed_total >= scan_lim
        count_source = "overview" if overview_total is not None and not has_where else "scan"
        sample_complete = exact_count and len(normalized) <= ret_lim and (not count_only or len(normalized) == 0)
        more_available = _derive_more_available(sample_complete=sample_complete, exact_count=exact_count, returned=len(items), total=total)
        if not exact_count and scan_limit_hit:
            more_available = "unknown" if has_where else True

        items = _project_user_items(items, fields)
        meta = _build_exhaustive_result_meta(
            base_meta={
                "scanned": observed_total,
                "total": total,
                "total_available": total_available,
                "total_matched": total_matched,
                "count_source": count_source,
                "lower_bound": bool(has_where and not exact_count),
                "overview_total": overview_total,
                "listed_total": observed_total,
                "overview_list_mismatch": overview_list_mismatch,
                "organization": org,
            },
            limit_plan=limit_plan,
            matched_count=len(normalized),
            returned_count=len(items),
            exact_count=exact_count,
            count_only=count_only,
            sample_complete=sample_complete,
            more_available=more_available,
            scan_limit_hit=scan_limit_hit,
        )
        return _helper_success(start_calls=start_calls, source=endpoint, items=items, meta=meta)

    async def hf_repo_search(
        query: str | None = None,
        repo_type: str | None = None,
        repo_types: list[str] | None = None,
        author: str | None = None,
        filters: list[str] | None = None,
        sort: str | None = None,
        limit: int = 20,
        where: dict[str, Any] | None = None,
        fields: list[str] | None = None,
        advanced: dict[str, Any] | None = None,
    ) -> dict[str, Any]:
        start_calls = call_count["n"]
        default_return = _policy_int("hf_repo_search", "default_return", 20)
        max_return = _policy_int("hf_repo_search", "max_return", SELECTIVE_ENDPOINT_RETURN_HARD_CAP)

        if repo_type is not None and repo_types is not None:
            return _helper_error(
                start_calls=start_calls,
                source="/api/repos",
                error="Pass either repo_type or repo_types, not both",
            )

        if repo_types is None:
            if repo_type is None or not str(repo_type).strip():
                requested_repo_types = ["model"]
            else:
                rt = _canonical_repo_type(repo_type, default="")
                if rt not in {"model", "dataset", "space"}:
                    return _helper_error(
                        start_calls=start_calls,
                        source="/api/repos",
                        error=f"Unsupported repo_type '{repo_type}'",
                    )
                requested_repo_types = [rt]
        else:
            raw_types = _coerce_str_list(repo_types)
            if not raw_types:
                return _helper_error(start_calls=start_calls, source="/api/repos", error="repo_types must not be empty")
            requested_repo_types: list[str] = []
            for raw in raw_types:
                rt = _canonical_repo_type(raw, default="")
                if rt not in {"model", "dataset", "space"}:
                    return _helper_error(
                        start_calls=start_calls,
                        source="/api/repos",
                        error=f"Unsupported repo_type '{raw}'",
                    )
                requested_repo_types.append(rt)

        filter_list = _coerce_str_list(filters)
        term = str(query or "").strip()
        author_clean = str(author or "").strip() or None
        requested_limit = limit
        lim = _clamp_int(limit, default=default_return, minimum=1, maximum=max_return)
        limit_meta = _derive_limit_metadata(
            requested_return_limit=requested_limit,
            applied_return_limit=lim,
            default_limit_used=limit == default_return,
        )
        hard_cap_applied = bool(limit_meta.get("hard_cap_applied"))

        if advanced is not None and not isinstance(advanced, dict):
            return _helper_error(start_calls=start_calls, source="/api/repos", error="advanced must be a dict when provided")
        if advanced is not None and len(requested_repo_types) != 1:
            return _helper_error(
                start_calls=start_calls,
                source="/api/repos",
                error="advanced may only be used with a single repo_type",
            )

        sort_keys: dict[str, str | None] = {}
        for rt in requested_repo_types:
            sort_key, sort_error = _normalize_repo_sort_key(rt, sort)
            if sort_error:
                return _helper_error(start_calls=start_calls, source=f"/api/{rt}s", error=sort_error)
            sort_keys[rt] = sort_key

        all_items: list[dict[str, Any]] = []
        scanned = 0
        source_endpoints: list[str] = []
        limit_boundary_hit = False
        api = _get_hf_api_client()

        for rt in requested_repo_types:
            endpoint = f"/api/{rt}s"
            source_endpoints.append(endpoint)
            extra_args = dict(advanced or {}) if len(requested_repo_types) == 1 else {}
            allowed_extra = _REPO_SEARCH_EXTRA_ARGS.get(rt, set())
            unsupported = sorted(str(k) for k in extra_args.keys() if str(k) not in allowed_extra)
            if unsupported:
                return _helper_error(
                    start_calls=start_calls,
                    source=endpoint,
                    error=(
                        f"Unsupported advanced args for repo_type='{rt}': {unsupported}. "
                        f"Allowed advanced args: {sorted(allowed_extra)}"
                    ),
                )
            if "card_data" in extra_args and "cardData" not in extra_args:
                extra_args["cardData"] = extra_args.pop("card_data")
            else:
                extra_args.pop("card_data", None)

            if not any(key in extra_args for key in ("expand", "full", "cardData", "fetch_config")):
                extra_args["expand"] = list(_REPO_SEARCH_DEFAULT_EXPAND[rt])

            try:
                if rt == "model":
                    payload = _host_hf_call(
                        endpoint,
                        lambda: list(
                            api.list_models(
                                search=term or None,
                                author=author_clean,
                                filter=filter_list or None,
                                sort=sort_keys[rt],  # type: ignore[arg-type]
                                limit=lim,
                                **extra_args,
                            )
                        ),
                    )
                elif rt == "dataset":
                    payload = _host_hf_call(
                        endpoint,
                        lambda: list(
                            api.list_datasets(
                                search=term or None,
                                author=author_clean,
                                filter=filter_list or None,
                                sort=sort_keys[rt],  # type: ignore[arg-type]
                                limit=lim,
                                **extra_args,
                            )
                        ),
                    )
                else:
                    payload = _host_hf_call(
                        endpoint,
                        lambda: list(
                            api.list_spaces(
                                search=term or None,
                                author=author_clean,
                                filter=filter_list or None,
                                sort=sort_keys[rt],  # type: ignore[arg-type]
                                limit=lim,
                                **extra_args,
                            )
                        ),
                    )
            except Exception as e:
                return _helper_error(start_calls=start_calls, source=endpoint, error=e)

            scanned += len(payload)
            if len(payload) >= lim:
                limit_boundary_hit = True
            all_items.extend(_normalize_repo_search_row(row, rt) for row in payload[:lim])

        all_items = _apply_where(all_items, where)
        combined_sort_key = next(iter(sort_keys.values()), None)
        all_items = _sort_repo_rows(all_items, combined_sort_key)
        matched = len(all_items)
        all_items = _project_repo_items(all_items[:lim], fields)
        more_available: bool | str = False
        truncated = False
        truncated_by = "none"
        next_request_hint: str | None = None
        if hard_cap_applied and scanned >= lim:
            truncated = True
            truncated_by = "hard_cap"
            more_available = "unknown"
            next_request_hint = f"Increase limit above {lim} to improve coverage"
        elif limit_boundary_hit:
            more_available = "unknown"
            next_request_hint = f"Increase limit above {lim} to check whether more rows exist"

        return _helper_success(
            start_calls=start_calls,
            source=",".join(source_endpoints),
            items=all_items,
            query=term or None,
            repo_types=requested_repo_types,
            filters=filter_list or None,
            sort=combined_sort_key,
            author=author_clean,
            limit=lim,
            scanned=scanned,
            matched=matched,
            returned=len(all_items),
            truncated=truncated,
            truncated_by=truncated_by,
            more_available=more_available,
            limit_boundary_hit=limit_boundary_hit,
            next_request_hint=next_request_hint,
            **limit_meta,
        )

    async def _user_graph_helper(
        kind: str,
        username: str,
        pro_only: bool | None,
        return_limit: int | None,
        scan_limit: int | None,
        count_only: bool,
        where: dict[str, Any] | None,
        fields: list[str] | None,
        *,
        helper_name: str,
    ) -> dict[str, Any]:
        start_calls = call_count["n"]
        default_return = _policy_int(helper_name, "default_return", 100)
        scan_cap = _policy_int(helper_name, "scan_max", GRAPH_SCAN_LIMIT_CAP)
        max_return = _policy_int(helper_name, "max_return", EXHAUSTIVE_HELPER_RETURN_HARD_CAP)

        u = str(username or "").strip()
        if not u:
            return _helper_error(start_calls=start_calls, source=f"/api/users/<u>/{kind}", error="username is required")

        limit_plan = _resolve_exhaustive_limits(
            return_limit=return_limit,
            count_only=count_only,
            default_return=default_return,
            max_return=max_return,
            scan_limit=scan_limit,
            scan_cap=scan_cap,
        )
        ret_lim = int(limit_plan["applied_return_limit"])
        scan_lim = int(limit_plan["applied_scan_limit"])
        has_where = isinstance(where, dict) and bool(where)
        filtered = (pro_only is not None) or has_where

        entity_type = "user"
        overview_total: int | None = None
        overview_source = f"/api/users/{u}/overview"
        if _budget_remaining() > 0:
            try:
                user_obj = _host_hf_call(overview_source, lambda: _get_hf_api_client().get_user_overview(u))
                overview_total = _as_int(user_obj.num_followers if kind == "followers" else user_obj.num_following)
            except Exception:
                org_overview_source = f"/api/organizations/{u}/overview"
                try:
                    org_obj = _host_hf_call(org_overview_source, lambda: _get_hf_api_client().get_organization_overview(u))
                except Exception:
                    overview_total = None
                else:
                    entity_type = "organization"
                    overview_source = org_overview_source
                    if kind != "followers":
                        return _helper_error(
                            start_calls=start_calls,
                            source=f"/api/organizations/{u}/{kind}",
                            error="organization graph only supports relation='followers'; organizations do not expose a following list",
                            relation=kind,
                            organization=u,
                            entity=u,
                            entity_type=entity_type,
                        )
                    overview_total = _as_int(getattr(org_obj, "num_followers", None))

        if count_only and not filtered and overview_total is not None:
            return _overview_count_only_success(
                start_calls=start_calls,
                source=overview_source,
                total=overview_total,
                limit_plan=limit_plan,
                base_meta={
                    "scanned": 1,
                    "count_source": "overview",
                    "relation": kind,
                    "pro_only": pro_only,
                    "where_applied": has_where,
                    "entity": u,
                    "entity_type": entity_type,
                    "username": u,
                    "organization": u if entity_type == "organization" else None,
                },
            )

        endpoint = f"/api/users/{u}/{kind}"
        try:
            if entity_type == "organization":
                endpoint = f"/api/organizations/{u}/followers"
                rows = _host_hf_call(endpoint, lambda: list(islice(_get_hf_api_client().list_organization_followers(u), scan_lim)))
            elif kind == "followers":
                rows = _host_hf_call(endpoint, lambda: list(islice(_get_hf_api_client().list_user_followers(u), scan_lim)))
            else:
                rows = _host_hf_call(endpoint, lambda: list(islice(_get_hf_api_client().list_user_following(u), scan_lim)))
        except Exception as e:
            return _helper_error(
                start_calls=start_calls,
                source=endpoint,
                error=e,
                relation=kind,
                username=u,
                entity=u,
                entity_type=entity_type,
                organization=u if entity_type == "organization" else None,
            )

        normalized: list[dict[str, Any]] = []
        for row in rows:
            handle = getattr(row, "username", None)
            if not isinstance(handle, str) or not handle:
                continue
            item = {
                "username": handle,
                "fullname": getattr(row, "fullname", None),
                "isPro": getattr(row, "is_pro", None),
            }
            if pro_only is True and item.get("isPro") is not True:
                continue
            if pro_only is False and item.get("isPro") is True:
                continue
            normalized.append(item)

        normalized = _apply_where(normalized, where)
        observed_total = len(rows)
        scan_exhaustive = observed_total < scan_lim

        overview_list_mismatch = (
            overview_total is not None
            and scan_exhaustive
            and observed_total != overview_total
        )

        if filtered:
            exact_count = scan_exhaustive
            total = len(normalized)
            total_matched = len(normalized)
        else:
            if overview_total is not None:
                exact_count = True
                total = overview_total
                total_matched = overview_total
            else:
                exact_count = scan_exhaustive
                total = observed_total
                total_matched = observed_total

        total_available = overview_total if overview_total is not None else observed_total
        items = normalized[:ret_lim]
        scan_limit_hit = not exact_count and observed_total >= scan_lim
        count_source = "overview" if overview_total is not None and not filtered else "scan"
        sample_complete = exact_count and len(normalized) <= ret_lim and (not count_only or len(normalized) == 0)
        more_available = _derive_more_available(sample_complete=sample_complete, exact_count=exact_count, returned=len(items), total=total)
        if not exact_count and scan_limit_hit:
            more_available = "unknown" if filtered else True

        items = _project_user_items(items, fields)
        meta = _build_exhaustive_result_meta(
            base_meta={
                "scanned": observed_total,
                "total": total,
                "total_available": total_available,
                "total_matched": total_matched,
                "count_source": count_source,
                "lower_bound": bool(filtered and not exact_count),
                "overview_total": overview_total,
                "listed_total": observed_total,
                "overview_list_mismatch": overview_list_mismatch,
                "relation": kind,
                "pro_only": pro_only,
                "where_applied": has_where,
                "entity": u,
                "entity_type": entity_type,
                "username": u,
                "organization": u if entity_type == "organization" else None,
            },
            limit_plan=limit_plan,
            matched_count=len(normalized),
            returned_count=len(items),
            exact_count=exact_count,
            count_only=count_only,
            sample_complete=sample_complete,
            more_available=more_available,
            scan_limit_hit=scan_limit_hit,
        )
        return _helper_success(
            start_calls=start_calls,
            source=endpoint,
            items=items,
            meta=meta,
        )

    async def hf_profile_summary(
        handle: str | None = None,
        include: list[str] | None = None,
        likes_limit: int = 10,
        activity_limit: int = 10,
    ) -> dict[str, Any]:
        start_calls = call_count["n"]
        resolved_handle, resolve_error = await _resolve_username_or_current(handle)
        if resolve_error:
            return _helper_error(start_calls=start_calls, source="/api/users/<u>/overview", error=resolve_error)
        if not isinstance(resolved_handle, str):
            return _helper_error(
                start_calls=start_calls,
                source="/api/users/<u>/overview",
                error="handle was not provided and current authenticated user could not be resolved",
            )

        try:
            requested_sections = (
                {part.lower() for part in _coerce_str_list(include) if part.strip()} if include is not None else set()
            )
        except ValueError as e:
            return _helper_error(
                start_calls=start_calls,
                source=f"/api/users/{resolved_handle}/overview",
                error=e,
            )
        invalid_sections = sorted(requested_sections - {"likes", "activity"})
        if invalid_sections:
            return _helper_error(
                start_calls=start_calls,
                source=f"/api/users/{resolved_handle}/overview",
                error=f"Unsupported include values: {invalid_sections}",
            )

        likes_lim = _clamp_int(likes_limit, default=10, minimum=0, maximum=OUTPUT_ITEMS_TRUNCATION_LIMIT)
        activity_lim = _clamp_int(activity_limit, default=10, minimum=0, maximum=OUTPUT_ITEMS_TRUNCATION_LIMIT)
        section_errors: dict[str, str] = {}

        user_overview = await hf_user_overview(resolved_handle)
        if user_overview.get("ok") is True:
            overview_item = _helper_item(user_overview) or {"username": resolved_handle}
            item: dict[str, Any] = {
                "handle": str(overview_item.get("username") or resolved_handle),
                "entity_type": "user",
                "display_name": overview_item.get("fullname") or str(overview_item.get("username") or resolved_handle),
                "bio": overview_item.get("bio"),
                "avatar_url": overview_item.get("avatarUrl"),
                "website_url": overview_item.get("websiteUrl"),
                "twitter_url": overview_item.get("twitter"),
                "github_url": overview_item.get("github"),
                "linkedin_url": overview_item.get("linkedin"),
                "bluesky_url": overview_item.get("bluesky"),
                "followers_count": _overview_count(overview_item, "followers"),
                "following_count": _overview_count(overview_item, "following"),
                "likes_count": _overview_count(overview_item, "likes"),
                "models_count": _overview_count(overview_item, "models"),
                "datasets_count": _overview_count(overview_item, "datasets"),
                "spaces_count": _overview_count(overview_item, "spaces"),
                "discussions_count": _overview_count(overview_item, "discussions"),
                "papers_count": _overview_count(overview_item, "papers"),
                "upvotes_count": _overview_count(overview_item, "upvotes"),
                "organizations": overview_item.get("orgs"),
                "is_pro": overview_item.get("isPro"),
            }

            if "likes" in requested_sections:
                likes = await hf_user_likes(
                    username=resolved_handle,
                    return_limit=likes_lim,
                    scan_limit=USER_SUMMARY_LIKES_SCAN_LIMIT,
                    count_only=likes_lim == 0,
                    sort="likedAt",
                    fields=["liked_at", "repo_id", "repo_type", "repo_author", "repo_url"],
                )
                item["likes_sample"] = likes.get("items") if likes.get("ok") is True else []
                if likes.get("ok") is not True:
                    section_errors["likes"] = str(likes.get("error") or "likes fetch failed")

            if "activity" in requested_sections:
                activity = await hf_recent_activity(
                    feed_type="user",
                    entity=resolved_handle,
                    return_limit=activity_lim,
                    max_pages=USER_SUMMARY_ACTIVITY_MAX_PAGES,
                    count_only=activity_lim == 0,
                    fields=["timestamp", "event_type", "repo_type", "repo_id"],
                )
                item["activity_sample"] = activity.get("items") if activity.get("ok") is True else []
                if activity.get("ok") is not True:
                    section_errors["activity"] = str(activity.get("error") or "activity fetch failed")

            return _helper_success(
                start_calls=start_calls,
                source=f"/api/users/{resolved_handle}/overview",
                items=[item],
                scanned=1,
                matched=1,
                returned=1,
                truncated=False,
                handle=resolved_handle,
                entity_type="user",
                include=sorted(requested_sections),
                likes_limit=likes_lim,
                activity_limit=activity_lim,
                section_errors=section_errors or None,
            )

        org_overview = await hf_org_overview(resolved_handle)
        if org_overview.get("ok") is True:
            overview_item = _helper_item(org_overview) or {"organization": resolved_handle}
            item = {
                "handle": str(overview_item.get("organization") or resolved_handle),
                "entity_type": "organization",
                "display_name": overview_item.get("displayName") or str(overview_item.get("organization") or resolved_handle),
                "description": overview_item.get("description"),
                "avatar_url": overview_item.get("avatarUrl"),
                "website_url": overview_item.get("websiteUrl"),
                "followers_count": _overview_count(overview_item, "followers"),
                "members_count": _overview_count(overview_item, "members"),
                "models_count": _overview_count(overview_item, "models"),
                "datasets_count": _overview_count(overview_item, "datasets"),
                "spaces_count": _overview_count(overview_item, "spaces"),
            }
            return _helper_success(
                start_calls=start_calls,
                source=f"/api/organizations/{resolved_handle}/overview",
                items=[item],
                scanned=1,
                matched=1,
                returned=1,
                truncated=False,
                handle=resolved_handle,
                entity_type="organization",
                include=[],
                ignored_includes=sorted(requested_sections) or None,
            )

        error = user_overview.get("error") or org_overview.get("error") or "profile fetch failed"
        return _helper_error(
            start_calls=start_calls,
            source=f"/api/profiles/{resolved_handle}",
            error=error,
            handle=resolved_handle,
        )

    async def hf_user_graph(
        username: str | None = None,
        relation: str = "followers",
        return_limit: int | None = None,
        scan_limit: int | None = None,
        count_only: bool = False,
        pro_only: bool | None = None,
        where: dict[str, Any] | None = None,
        fields: list[str] | None = None,
    ) -> dict[str, Any]:
        start_calls = call_count["n"]
        rel = str(relation or "").strip().lower() or "followers"
        if rel not in {"followers", "following"}:
            return _helper_error(
                start_calls=start_calls,
                source="/api/users/<u>/followers",
                error="relation must be 'followers' or 'following'",
            )

        resolved_username, resolve_error = await _resolve_username_or_current(username)
        if resolve_error:
            return _helper_error(start_calls=start_calls, source=f"/api/users/<u>/{rel}", error=resolve_error, relation=rel)
        if not isinstance(resolved_username, str):
            return _helper_error(start_calls=start_calls, source=f"/api/users/<u>/{rel}", error="username is required", relation=rel)

        return await _user_graph_helper(
            rel,
            resolved_username,
            pro_only,
            return_limit,
            scan_limit,
            count_only,
            where,
            fields,
            helper_name="hf_user_graph",
        )

    async def hf_user_likes(
        username: str | None = None,
        repo_types: list[str] | None = None,
        return_limit: int | None = None,
        scan_limit: int | None = None,
        count_only: bool = False,
        where: dict[str, Any] | None = None,
        fields: list[str] | None = None,
        sort: str | None = None,
        ranking_window: int | None = None,
    ) -> dict[str, Any]:
        start_calls = call_count["n"]
        default_return = _policy_int("hf_user_likes", "default_return", 100)
        scan_cap = _policy_int("hf_user_likes", "scan_max", LIKES_SCAN_LIMIT_CAP)
        ranking_default = _policy_int("hf_user_likes", "ranking_default", LIKES_RANKING_WINDOW_DEFAULT)
        enrich_cap = _policy_int("hf_user_likes", "enrich_max", LIKES_ENRICHMENT_MAX_REPOS)

        resolved_username, resolve_error = await _resolve_username_or_current(username)
        if resolve_error:
            return _helper_error(start_calls=start_calls, source="/api/users/<u>/likes", error=resolve_error)
        if not isinstance(resolved_username, str):
            return _helper_error(start_calls=start_calls, source="/api/users/<u>/likes", error="username is required")

        sort_key, sort_error = _normalize_user_likes_sort(sort)
        if sort_error:
            return _helper_error(start_calls=start_calls, source=f"/api/users/{resolved_username}/likes", error=sort_error)
        if sort_key is None:
            return _helper_error(
                start_calls=start_calls,
                source=f"/api/users/{resolved_username}/likes",
                error="sort must be one of likedAt, repoLikes, repoDownloads",
            )

        limit_plan = _resolve_exhaustive_limits(
            return_limit=return_limit,
            count_only=count_only,
            default_return=default_return,
            max_return=EXHAUSTIVE_HELPER_RETURN_HARD_CAP,
            scan_limit=scan_limit,
            scan_cap=scan_cap,
        )
        ret_lim = int(limit_plan["applied_return_limit"])
        scan_lim = int(limit_plan["applied_scan_limit"])

        allowed_repo_types: set[str] | None = None
        try:
            raw_repo_types: list[str] = _coerce_str_list(repo_types) if repo_types is not None else []
        except ValueError as e:
            return _helper_error(start_calls=start_calls, source=f"/api/users/{resolved_username}/likes", error=e)
        if raw_repo_types:
            allowed_repo_types = set()
            for raw in raw_repo_types:
                canonical = _canonical_repo_type(raw, default="")
                if canonical not in {"model", "dataset", "space"}:
                    return _helper_error(
                        start_calls=start_calls,
                        source=f"/api/users/{resolved_username}/likes",
                        error=f"Unsupported repo_type '{raw}'",
                    )
                allowed_repo_types.add(canonical)

        endpoint = f"/api/users/{resolved_username}/likes"
        resp = _host_raw_call(endpoint, params={"limit": scan_lim})
        if not resp.get("ok"):
            return _helper_error(
                start_calls=start_calls,
                source=endpoint,
                error=resp.get("error") or "likes fetch failed",
            )

        payload = resp.get("data") if isinstance(resp.get("data"), list) else []
        scanned_rows = payload[:scan_lim]
        matched_rows: list[tuple[int, dict[str, Any]]] = []

        for row in scanned_rows:
            if not isinstance(row, dict):
                continue
            repo = row.get("repo") if isinstance(row.get("repo"), dict) else {}
            repo_data = row.get("repoData") if isinstance(row.get("repoData"), dict) else {}

            repo_id = repo_data.get("id") or repo_data.get("name") or repo.get("name")
            if not isinstance(repo_id, str) or not repo_id:
                continue

            repo_type = _canonical_repo_type(repo_data.get("type") or repo.get("type"), default="")
            if not repo_type:
                repo_type = _canonical_repo_type(repo.get("type"), default="model")
            if allowed_repo_types is not None and repo_type not in allowed_repo_types:
                continue

            repo_author = repo_data.get("author")
            if not isinstance(repo_author, str) and "/" in repo_id:
                repo_author = repo_id.split("/", 1)[0]

            item = {
                "likedAt": row.get("likedAt") or row.get("createdAt"),
                "liked_at": row.get("likedAt") or row.get("createdAt"),
                "repoId": repo_id,
                "repo_id": repo_id,
                "repoType": repo_type,
                "repo_type": repo_type,
                "repoAuthor": repo_author,
                "repo_author": repo_author,
                "repoLikes": _as_int(repo_data.get("likes")),
                "repo_likes": _as_int(repo_data.get("likes")),
                "repoDownloads": _as_int(repo_data.get("downloads")),
                "repo_downloads": _as_int(repo_data.get("downloads")),
                "likes": _as_int(repo_data.get("likes")),
                "downloads": _as_int(repo_data.get("downloads")),
                "repo_url": _repo_web_url(repo_type, repo_id),
            }
            if not _item_matches_where(item, where):
                continue
            matched_rows.append((len(matched_rows), item))

        matched = len(matched_rows)
        scan_exhaustive = len(payload) < scan_lim
        exact_count = scan_exhaustive
        total_matched = matched
        total = total_matched
        effective_ranking_window: int | None = None
        ranking_complete = sort_key == "likedAt" and exact_count
        enriched = 0

        selected_pairs: list[tuple[int, dict[str, Any]]]
        if count_only:
            selected_pairs = []
            ranking_complete = False if matched > 0 else exact_count
        elif sort_key == "likedAt":
            selected_pairs = matched_rows[:ret_lim]
        else:
            metric = str(sort_key)
            requested_window = ranking_window if ranking_window is not None else ranking_default
            effective_ranking_window = _clamp_int(
                requested_window,
                default=ranking_default,
                minimum=1,
                maximum=enrich_cap,
            )
            shortlist_size = min(effective_ranking_window, matched, scan_lim)
            shortlist = matched_rows[:shortlist_size]
            candidates = [
                pair
                for pair in shortlist
                if pair[1].get(metric) is None
                and isinstance(pair[1].get("repoId"), str)
                and pair[1].get("repoType") in {"model", "dataset", "space"}
            ]
            enrich_budget = min(len(candidates), _budget_remaining(), shortlist_size)
            for _, item in candidates[:enrich_budget]:
                repo_type = str(item.get("repoType"))
                repo_id = str(item.get("repoId"))
                detail_endpoint = f"/api/{_canonical_repo_type(repo_type)}s/{repo_id}"
                try:
                    detail = _host_hf_call(
                        detail_endpoint,
                        lambda rt=repo_type, rid=repo_id: (
                            _get_hf_api_client().model_info(rid)
                            if _canonical_repo_type(rt) == "model"
                            else _get_hf_api_client().dataset_info(rid)
                            if _canonical_repo_type(rt) == "dataset"
                            else _get_hf_api_client().space_info(rid)
                        ),
                    )
                except Exception:
                    continue

                likes = _as_int(getattr(detail, "likes", None))
                downloads = _as_int(getattr(detail, "downloads", None))
                if likes is not None:
                    item["repoLikes"] = likes
                    item["repo_likes"] = likes
                    item["likes"] = likes
                if downloads is not None:
                    item["repoDownloads"] = downloads
                    item["repo_downloads"] = downloads
                    item["downloads"] = downloads
                enriched += 1

            def _ranking_key(pair: tuple[int, dict[str, Any]]) -> tuple[int, int, int]:
                idx, row = pair
                metric_value = _as_int(row.get(metric))
                if metric_value is None:
                    return (1, 0, idx)
                return (0, -metric_value, idx)

            ranked_shortlist = sorted(shortlist, key=_ranking_key)
            selected_pairs = ranked_shortlist[:ret_lim]
            ranking_complete = exact_count and shortlist_size >= matched and len(candidates) <= enrich_budget

        items = _project_items([row for _, row in selected_pairs], fields)
        popularity_present = sum(1 for _, row in selected_pairs if row.get("repoLikes") is not None)
        sample_complete = (
            exact_count
            and ret_lim >= matched
            and (sort_key == "likedAt" or ranking_complete)
            and (not count_only or matched == 0)
        )
        scan_limit_hit = not scan_exhaustive and len(payload) >= scan_lim
        more_available = _derive_more_available(sample_complete=sample_complete, exact_count=exact_count, returned=len(items), total=total)
        if scan_limit_hit:
            more_available = "unknown" if (allowed_repo_types is not None or where) else True

        meta = _build_exhaustive_result_meta(
            base_meta={
                "scanned": len(scanned_rows),
                "total": total,
                "total_available": len(payload),
                "total_matched": total_matched,
                "count_source": "scan",
                "lower_bound": not exact_count,
                "enriched": enriched,
                "popularity_present": popularity_present,
                "sort_applied": sort_key,
                "ranking_window": effective_ranking_window,
                "ranking_complete": ranking_complete,
                "username": resolved_username,
            },
            limit_plan=limit_plan,
            matched_count=matched,
            returned_count=len(items),
            exact_count=exact_count,
            count_only=count_only,
            sample_complete=sample_complete,
            more_available=more_available,
            scan_limit_hit=scan_limit_hit,
            truncated_extra=sort_key != "likedAt" and not ranking_complete,
        )
        return _helper_success(
            start_calls=start_calls,
            source=endpoint,
            items=items,
            meta=meta,
        )

    async def hf_repo_likers(
        repo_id: str,
        repo_type: str,
        return_limit: int | None = None,
        count_only: bool = False,
        pro_only: bool | None = None,
        where: dict[str, Any] | None = None,
        fields: list[str] | None = None,
    ) -> dict[str, Any]:
        start_calls = call_count["n"]
        rid = str(repo_id or "").strip()
        if not rid:
            return _helper_error(start_calls=start_calls, source="/api/repos/<repo>/likers", error="repo_id is required")

        rt = _canonical_repo_type(repo_type, default="")
        if rt not in {"model", "dataset", "space"}:
            return _helper_error(
                start_calls=start_calls,
                source=f"/api/repos/{rid}/likers",
                error=f"Unsupported repo_type '{repo_type}'",
                repo_id=rid,
            )

        default_return = _policy_int("hf_repo_likers", "default_return", 1_000)
        requested_return_limit = return_limit
        default_limit_used = requested_return_limit is None and not count_only
        has_where = isinstance(where, dict) and bool(where)

        endpoint = f"/api/{rt}s/{rid}/likers"
        resp = _host_raw_call(endpoint)
        if not resp.get("ok"):
            return _helper_error(
                start_calls=start_calls,
                source=endpoint,
                error=resp.get("error") or "repo likers fetch failed",
                repo_id=rid,
                repo_type=rt,
            )

        payload = resp.get("data") if isinstance(resp.get("data"), list) else []
        normalized: list[dict[str, Any]] = []
        for row in payload:
            if not isinstance(row, dict):
                continue
            username = row.get("user") or row.get("username")
            if not isinstance(username, str) or not username:
                continue
            item = {
                "username": username,
                "fullname": row.get("fullname"),
                "type": row.get("type") if isinstance(row.get("type"), str) and row.get("type") else "user",
                "isPro": row.get("isPro"),
            }
            if pro_only is True and item.get("isPro") is not True:
                continue
            if pro_only is False and item.get("isPro") is True:
                continue
            if not _item_matches_where(item, where):
                continue
            normalized.append(item)

        # /likers is a one-shot full-list endpoint: the Hub returns the liker rows in a
        # single response with no cursor/scan continuation. Keep the default output compact,
        # but do not apply the generic exhaustive hard cap here because it does not improve
        # upstream coverage or cost; the full liker set has already been fetched.
        if count_only:
            ret_lim = 0
        elif requested_return_limit is None:
            ret_lim = default_return
        else:
            try:
                ret_lim = max(0, int(requested_return_limit))
            except Exception:
                ret_lim = default_return
        limit_plan = {
            "requested_return_limit": requested_return_limit,
            "applied_return_limit": ret_lim,
            "default_limit_used": default_limit_used,
            "hard_cap_applied": False,
        }

        matched = len(normalized)
        items = [] if count_only else normalized[:ret_lim]
        return_limit_hit = ret_lim > 0 and matched > ret_lim
        truncated_by = _derive_truncated_by(
            hard_cap=False,
            return_limit_hit=return_limit_hit,
        )
        sample_complete = matched <= ret_lim and (not count_only or matched == 0)
        truncated = truncated_by != "none"
        more_available = _derive_more_available(
            sample_complete=sample_complete,
            exact_count=True,
            returned=len(items),
            total=matched,
        )

        items = _project_actor_items(items, fields)

        meta = _build_exhaustive_meta(
            base_meta={
                "scanned": len(payload),
                "matched": matched,
                "returned": len(items),
                "total": matched,
                "total_available": len(payload),
                "total_matched": matched,
                "truncated": truncated,
                "count_source": "likers_list",
                "lower_bound": False,
                "repo_id": rid,
                "repo_type": rt,
                "pro_only": pro_only,
                "where_applied": has_where,
                "upstream_pagination": "none",
            },
            limit_plan=limit_plan,
            sample_complete=sample_complete,
            exact_count=True,
            truncated_by=truncated_by,
            more_available=more_available,
        )
        meta["hard_cap_applied"] = False
        return _helper_success(
            start_calls=start_calls,
            source=endpoint,
            items=items,
            meta=meta,
        )

    async def hf_recent_activity(
        feed_type: str | None = None,
        entity: str | None = None,
        activity_types: list[str] | None = None,
        repo_types: list[str] | None = None,
        return_limit: int | None = None,
        max_pages: int | None = None,
        start_cursor: str | None = None,
        count_only: bool = False,
        where: dict[str, Any] | None = None,
        fields: list[str] | None = None,
    ) -> dict[str, Any]:
        start_calls = call_count["n"]
        default_return = _policy_int("hf_recent_activity", "default_return", 100)
        page_cap = _policy_int("hf_recent_activity", "page_limit", RECENT_ACTIVITY_PAGE_SIZE)
        pages_cap = _policy_int("hf_recent_activity", "max_pages", RECENT_ACTIVITY_SCAN_MAX_PAGES)

        requested_max_pages = max_pages

        ft = str(feed_type or "").strip().lower()
        ent = str(entity or "").strip()
        if ft not in {"user", "org"}:
            if ft and not ent:
                ent = ft
                ft = "user"
            elif not ft and ent:
                ft = "user"

        if ft not in {"user", "org"}:
            return _helper_error(start_calls=start_calls, source="/api/recent-activity", error="feed_type must be 'user' or 'org'")
        if not ent:
            return _helper_error(start_calls=start_calls, source="/api/recent-activity", error="entity is required")

        limit_plan = _resolve_exhaustive_limits(
            return_limit=return_limit,
            count_only=count_only,
            default_return=default_return,
            max_return=EXHAUSTIVE_HELPER_RETURN_HARD_CAP,
        )
        ret_lim = int(limit_plan["applied_return_limit"])
        page_lim = page_cap
        pages_lim = _clamp_int(requested_max_pages, default=pages_cap, minimum=1, maximum=pages_cap)

        type_filter = {str(t).strip().lower() for t in (activity_types or []) if str(t).strip()}
        repo_filter = {_canonical_repo_type(t, default="") for t in (repo_types or []) if str(t).strip()}

        next_cursor = str(start_cursor).strip() if isinstance(start_cursor, str) and start_cursor.strip() else None
        items: list[dict[str, Any]] = []
        scanned = 0
        matched = 0
        pages = 0
        exhausted_feed = False
        stopped_for_budget = False

        while pages < pages_lim and (ret_lim == 0 or len(items) < ret_lim):
            if _budget_remaining() <= 0:
                stopped_for_budget = True
                break

            params: dict[str, Any] = {"feedType": ft, "entity": ent, "limit": page_lim}
            if next_cursor:
                params["cursor"] = next_cursor

            resp = _host_raw_call("/api/recent-activity", params=params)
            if not resp.get("ok"):
                if pages == 0:
                    return _helper_error(
                        start_calls=start_calls,
                        source="/api/recent-activity",
                        error=resp.get("error") or "recent-activity fetch failed",
                    )
                break

            payload = resp.get("data") if isinstance(resp.get("data"), dict) else {}
            rows = payload.get("recentActivity") if isinstance(payload.get("recentActivity"), list) else []
            cursor_raw = payload.get("cursor")
            next_cursor = cursor_raw if isinstance(cursor_raw, str) and cursor_raw else None
            pages += 1

            if not rows:
                exhausted_feed = True
                break

            for row in rows:
                if not isinstance(row, dict):
                    continue
                scanned += 1

                typ = str(row.get("type") or "").strip().lower()
                repo_id = row.get("repoId")
                repo_type = row.get("repoType")
                repo_data = row.get("repoData") if isinstance(row.get("repoData"), dict) else None
                repo_obj = row.get("repo") if isinstance(row.get("repo"), dict) else None
                if repo_id is None and repo_data is not None:
                    repo_id = repo_data.get("id") or repo_data.get("name")
                if repo_id is None and repo_obj is not None:
                    repo_id = repo_obj.get("id") or repo_obj.get("name")
                if repo_type is None and repo_data is not None:
                    repo_type = repo_data.get("type")
                if repo_type is None and repo_obj is not None:
                    repo_type = repo_obj.get("type")

                rt = _canonical_repo_type(repo_type, default="") if repo_type else ""
                if type_filter and typ not in type_filter:
                    continue
                if repo_filter and rt not in repo_filter:
                    continue

                item = {
                    "time": row.get("time"),
                    "timestamp": row.get("time"),
                    "type": row.get("type"),
                    "event_type": row.get("type"),
                    "repoType": rt or repo_type,
                    "repo_type": rt or repo_type,
                    "repoId": repo_id,
                    "repo_id": repo_id,
                }
                if not _item_matches_where(item, where):
                    continue

                matched += 1
                if len(items) < ret_lim:
                    items.append(item)

            if not next_cursor:
                exhausted_feed = True
                break

        items = _project_items(items, fields)
        exact_count = exhausted_feed and not stopped_for_budget
        sample_complete = exact_count and ret_lim >= matched and (not count_only or matched == 0)
        page_limit_hit = next_cursor is not None and pages >= pages_lim and not exhausted_feed
        more_available: bool | str = _derive_more_available(sample_complete=sample_complete, exact_count=exact_count, returned=len(items), total=matched if exact_count else None)
        if next_cursor is not None:
            more_available = True
        elif stopped_for_budget and not exact_count:
            more_available = "unknown"

        meta = _build_exhaustive_result_meta(
            base_meta={
                "scanned": scanned,
                "total": matched,
                "total_matched": matched,
                "pages": pages,
                "count_source": "scan" if exact_count else "none",
                "lower_bound": not exact_count,
                "page_limit": page_lim,
                "stopped_for_budget": stopped_for_budget,
                "feed_type": ft,
                "entity": ent,
            },
            limit_plan=limit_plan,
            matched_count=matched,
            returned_count=len(items),
            exact_count=exact_count,
            count_only=count_only,
            sample_complete=sample_complete,
            more_available=more_available,
            page_limit_hit=page_limit_hit,
            truncated_extra=stopped_for_budget,
            requested_max_pages=requested_max_pages,
            applied_max_pages=pages_lim,
        )
        return _helper_success(
            start_calls=start_calls,
            source="/api/recent-activity",
            items=items,
            meta=meta,
            cursor=next_cursor,
        )

    async def hf_repo_discussions(repo_type: str, repo_id: str, limit: int = 20) -> dict[str, Any]:
        start_calls = call_count["n"]
        rt = _canonical_repo_type(repo_type)
        rid = str(repo_id or "").strip()
        if "/" not in rid:
            return _helper_error(start_calls=start_calls, source="/api/.../discussions", error="repo_id must be owner/name")

        lim = _clamp_int(limit, default=20, minimum=1, maximum=SELECTIVE_ENDPOINT_RETURN_HARD_CAP)
        endpoint = f"/api/{rt}s/{rid}/discussions"
        try:
            discussions = _host_hf_call(
                endpoint,
                lambda: list(islice(_get_hf_api_client().get_repo_discussions(repo_id=rid, repo_type=rt), lim)),
            )
        except Exception as e:
            return _helper_error(start_calls=start_calls, source=endpoint, error=e)

        items: list[dict[str, Any]] = []
        for d in discussions:
            num = _as_int(getattr(d, "num", None))
            items.append(
                {
                    "num": num,
                    "number": num,
                    "discussionNum": num,
                    "id": num,
                    "title": getattr(d, "title", None),
                    "author": getattr(d, "author", None),
                    "createdAt": str(getattr(d, "created_at", None)) if getattr(d, "created_at", None) is not None else None,
                    "status": getattr(d, "status", None),
                }
            )

        return _helper_success(
            start_calls=start_calls,
            source=endpoint,
            items=items,
            scanned=len(items),
            matched=len(items),
            returned=len(items),
            truncated=False,
            total_count=None,
        )

    async def hf_repo_discussion_details(repo_type: str, repo_id: str, discussion_num: int) -> dict[str, Any]:
        start_calls = call_count["n"]
        rt = _canonical_repo_type(repo_type)
        rid = str(repo_id or "").strip()
        if "/" not in rid:
            return _helper_error(start_calls=start_calls, source="/api/.../discussions/<num>", error="repo_id must be owner/name")

        num = _as_int(discussion_num)
        if num is None:
            return _helper_error(
                start_calls=start_calls,
                source=f"/api/{rt}s/{rid}/discussions/<num>",
                error="discussion_num must be an integer",
            )

        endpoint = f"/api/{rt}s/{rid}/discussions/{num}"
        try:
            detail = _host_hf_call(
                endpoint,
                lambda: _get_hf_api_client().get_discussion_details(
                    repo_id=rid,
                    discussion_num=int(num),
                    repo_type=rt,
                ),
            )
        except Exception as e:
            return _helper_error(start_calls=start_calls, source=endpoint, error=e)

        comment_events: list[dict[str, Any]] = []
        raw_events = getattr(detail, "events", None)
        if isinstance(raw_events, list):
            for event in raw_events:
                if str(getattr(event, "type", "")).strip().lower() != "comment":
                    continue
                comment_events.append(
                    {
                        "author": getattr(event, "author", None),
                        "createdAt": _dt_to_str(getattr(event, "created_at", None)),
                        "text": getattr(event, "content", None),
                        "rendered": getattr(event, "rendered", None),
                    }
                )

        latest_comment: dict[str, Any] | None = None
        if comment_events:
            latest_comment = max(comment_events, key=lambda row: str(row.get("createdAt") or ""))

        item: dict[str, Any] = {
            "num": num,
            "number": num,
            "discussionNum": num,
            "id": num,
            "repo_id": rid,
            "repo_type": rt,
            "title": getattr(detail, "title", None),
            "author": getattr(detail, "author", None),
            "createdAt": _dt_to_str(getattr(detail, "created_at", None)),
            "status": getattr(detail, "status", None),
            "url": getattr(detail, "url", None),
            "commentCount": len(comment_events),
            "latestCommentAuthor": latest_comment.get("author") if latest_comment else None,
            "latestCommentCreatedAt": latest_comment.get("createdAt") if latest_comment else None,
            "latestCommentText": latest_comment.get("text") if latest_comment else None,
            "latestCommentHtml": latest_comment.get("rendered") if latest_comment else None,
            "latest_comment_author": latest_comment.get("author") if latest_comment else None,
            "latest_comment_created_at": latest_comment.get("createdAt") if latest_comment else None,
            "latest_comment_text": latest_comment.get("text") if latest_comment else None,
            "latest_comment_html": latest_comment.get("rendered") if latest_comment else None,
        }

        return _helper_success(
            start_calls=start_calls,
            source=endpoint,
            items=[item],
            scanned=len(comment_events),
            matched=1,
            returned=1,
            truncated=False,
            total_comments=len(comment_events),
        )

    def _resolve_repo_detail_row(
        api: HfApi,
        repo_id: str,
        attempt_types: list[str],
    ) -> tuple[dict[str, Any] | None, dict[str, Any] | None]:
        rid = str(repo_id or "").strip()
        if "/" not in rid:
            return None, {"repo_id": rid, "error": "repo_id must be owner/name"}

        resolved_type: str | None = None
        detail: Any = None
        last_endpoint = "/api/repos"
        errors: list[str] = []

        for rt in attempt_types:
            endpoint = f"/api/{rt}s/{rid}"
            last_endpoint = endpoint
            try:
                detail = _host_hf_call(
                    endpoint,
                    lambda rt=rt, rid=rid: api.model_info(rid)
                    if rt == "model"
                    else api.dataset_info(rid)
                    if rt == "dataset"
                    else api.space_info(rid),
                )
                resolved_type = rt
                break
            except Exception as e:
                errors.append(f"{rt}: {str(e)}")

        if resolved_type is None or detail is None:
            return None, {
                "repo_id": rid,
                "error": "; ".join(errors[:3]) if errors else "repo lookup failed",
                "attempted_repo_types": list(attempt_types),
                "source": last_endpoint,
            }

        return _normalize_repo_detail_row(detail, resolved_type, rid), None

    async def hf_repo_details(
        repo_id: str | None = None,
        repo_ids: list[str] | None = None,
        repo_type: str = "auto",
        fields: list[str] | None = None,
    ) -> dict[str, Any]:
        start_calls = call_count["n"]

        if repo_id is not None and repo_ids is not None:
            return _helper_error(
                start_calls=start_calls,
                source="/api/repos",
                error="Pass either repo_id or repo_ids, not both",
            )

        requested_ids = [str(repo_id).strip()] if isinstance(repo_id, str) and str(repo_id).strip() else []
        if repo_ids is not None:
            requested_ids = _coerce_str_list(repo_ids)

        if not requested_ids:
            return _helper_error(start_calls=start_calls, source="/api/repos", error="repo_id or repo_ids is required")

        raw_type = str(repo_type or "auto").strip().lower()
        if raw_type in {"", "auto"}:
            base_attempt_types = ["model", "dataset", "space"]
        else:
            canonical_type = _canonical_repo_type(raw_type, default="")
            if canonical_type not in {"model", "dataset", "space"}:
                return _helper_error(
                    start_calls=start_calls,
                    source="/api/repos",
                    error=f"Unsupported repo_type '{repo_type}'",
                )
            base_attempt_types = [canonical_type]

        api = _get_hf_api_client()
        items: list[dict[str, Any]] = []
        failures: list[dict[str, Any]] = []

        for rid in requested_ids:
            row, failure = _resolve_repo_detail_row(api, rid, base_attempt_types)
            if row is None:
                if failure is not None:
                    failures.append(failure)
                continue
            items.append(row)

        if not items:
            summary = failures[0]["error"] if failures else "repo lookup failed"
            return _helper_error(
                start_calls=start_calls,
                source="/api/repos",
                error=summary,
                failures=failures,
                repo_type=repo_type,
            )

        items = _project_repo_items(items, fields)
        return _helper_success(
            start_calls=start_calls,
            source="/api/repos",
            items=items,
            repo_type=repo_type,
            requested_repo_ids=requested_ids,
            failures=failures or None,
            matched=len(items),
            returned=len(items),
        )

    async def hf_trending(
        repo_type: str = "model",
        limit: int = 20,
        where: dict[str, Any] | None = None,
        fields: list[str] | None = None,
    ) -> dict[str, Any]:
        start_calls = call_count["n"]
        default_return = _policy_int("hf_trending", "default_return", 20)
        max_return = _policy_int("hf_trending", "max_return", TRENDING_ENDPOINT_MAX_LIMIT)

        raw_type = str(repo_type or "model").strip().lower()
        if raw_type == "all":
            requested_type = "all"
        else:
            requested_type = _canonical_repo_type(raw_type, default="")
            if requested_type not in {"model", "dataset", "space"}:
                return _helper_error(
                    start_calls=start_calls,
                    source="/api/trending",
                    error=f"Unsupported repo_type '{repo_type}'",
                )
        lim = _clamp_int(limit, default=default_return, minimum=1, maximum=max_return)

        resp = _host_raw_call("/api/trending", params={"type": requested_type, "limit": lim})
        if not resp.get("ok"):
            return _helper_error(start_calls=start_calls, source="/api/trending", error=resp.get("error") or "trending fetch failed")

        payload = resp.get("data") if isinstance(resp.get("data"), dict) else {}
        rows = payload.get("recentlyTrending") if isinstance(payload.get("recentlyTrending"), list) else []

        items: list[dict[str, Any]] = []
        default_row_type = requested_type if requested_type != "all" else "model"
        for idx, row in enumerate(rows[:lim], start=1):
            if not isinstance(row, dict):
                continue
            repo = row.get("repoData") if isinstance(row.get("repoData"), dict) else {}
            items.append(_normalize_trending_row(repo, default_row_type, rank=idx))

        api = _get_hf_api_client()
        enriched_items: list[dict[str, Any]] = []
        enrichment_failures: list[dict[str, Any]] = []
        for item in items:
            repo_id = item.get("repo_id")
            if not isinstance(repo_id, str) or not repo_id:
                enriched_items.append(item)
                continue

            item_repo_type = item.get("repo_type")
            if isinstance(item_repo_type, str) and item_repo_type in {"model", "dataset", "space"}:
                attempt_types = [item_repo_type]
            else:
                attempt_types = ["model", "dataset", "space"]

            detail_row, failure = _resolve_repo_detail_row(api, repo_id, attempt_types)
            if detail_row is None:
                enriched_items.append(item)
                if failure is not None:
                    enrichment_failures.append(failure)
                continue

            merged = dict(detail_row)
            trending_score = item.get("trending_score")
            if trending_score is not None:
                merged["trending_score"] = trending_score
            if item.get("trending_rank") is not None:
                merged["trending_rank"] = item.get("trending_rank")
            enriched_items.append(merged)

        items = enriched_items
        items = _apply_where(items, where)
        matched = len(items)
        items = _project_repo_items(items[:lim], fields)

        return _helper_success(
            start_calls=start_calls,
            source="/api/trending",
            items=items,
            repo_type=requested_type,
            limit=lim,
            scanned=len(rows),
            matched=matched,
            returned=len(items),
            trending_score_available=any(item.get("trending_score") is not None for item in items),
            ordered_ranking=True,
            failures=enrichment_failures or None,
        )

    async def hf_collections_search(
        query: str | None = None,
        owner: str | None = None,
        return_limit: int = 20,
        count_only: bool = False,
        where: dict[str, Any] | None = None,
        fields: list[str] | None = None,
    ) -> dict[str, Any]:
        start_calls = call_count["n"]
        default_return = _policy_int("hf_collections_search", "default_return", 20)
        max_return = _policy_int("hf_collections_search", "max_return", OUTPUT_ITEMS_TRUNCATION_LIMIT)

        if count_only:
            return_limit = 0

        lim = _clamp_int(return_limit, default=default_return, minimum=0, maximum=max_return)
        owner_clean = str(owner or "").strip() or None
        fetch_lim = max_return if lim == 0 or owner_clean else lim
        if owner_clean:
            fetch_lim = min(fetch_lim, 100)

        term = str(query or "").strip()
        if not term and owner_clean:
            term = owner_clean
        if not term:
            return _helper_error(start_calls=start_calls, source="/api/collections", error="query or owner is required")

        params: dict[str, Any] = {"limit": fetch_lim}
        if term:
            params["q"] = term
        if owner_clean:
            params["owner"] = owner_clean

        resp = _host_raw_call("/api/collections", params=params)
        if not resp.get("ok"):
            return _helper_error(
                start_calls=start_calls,
                source="/api/collections",
                error=resp.get("error") or "collections fetch failed",
            )

        payload = resp.get("data") if isinstance(resp.get("data"), list) else []
        items: list[dict[str, Any]] = []
        for row in payload[:fetch_lim]:
            if not isinstance(row, dict):
                continue
            owner = _author_from_any(row.get("owner")) or _author_from_any(row.get("ownerData"))
            if not owner and isinstance(row.get("slug"), str) and "/" in str(row.get("slug")):
                owner = str(row.get("slug")).split("/", 1)[0]
            if owner_clean is not None and owner != owner_clean:
                continue

            owner_payload = row.get("owner") if isinstance(row.get("owner"), dict) else {}
            collection_items = row.get("items") if isinstance(row.get("items"), list) else []
            slug = row.get("slug")
            items.append(
                {
                    "collection_id": slug,
                    "slug": slug,
                    "title": row.get("title"),
                    "owner": owner,
                    "owner_type": owner_payload.get("type") if isinstance(owner_payload.get("type"), str) else None,
                    "description": row.get("description"),
                    "gating": row.get("gating"),
                    "last_updated": row.get("lastUpdated"),
                    "item_count": len(collection_items),
                }
            )

        items = _apply_where(items, where)
        total_matched = len(items)
        items = items[:lim]
        items = _project_collection_items(items, fields)
        truncated = (lim > 0 and total_matched > lim) or (lim == 0 and len(payload) >= fetch_lim)

        return _helper_success(
            start_calls=start_calls,
            source="/api/collections",
            items=items,
            scanned=len(payload),
            matched=total_matched,
            returned=len(items),
            total=len(payload),
            total_matched=total_matched,
            total_population=len(payload),
            truncated=truncated,
            complete=not truncated,
            query=term,
            owner=owner_clean,
        )

    async def hf_collection_items(
        collection_id: str,
        repo_types: list[str] | None = None,
        return_limit: int = 100,
        count_only: bool = False,
        where: dict[str, Any] | None = None,
        fields: list[str] | None = None,
    ) -> dict[str, Any]:
        start_calls = call_count["n"]
        default_return = _policy_int("hf_collection_items", "default_return", 100)
        max_return = _policy_int("hf_collection_items", "max_return", OUTPUT_ITEMS_TRUNCATION_LIMIT)

        cid = str(collection_id or "").strip()
        if not cid:
            return _helper_error(
                start_calls=start_calls,
                source="/api/collections/<collection_id>",
                error="collection_id is required",
            )

        if count_only:
            return_limit = 0

        lim = _clamp_int(return_limit, default=default_return, minimum=0, maximum=max_return)

        allowed_repo_types: set[str] | None = None
        try:
            raw_repo_types = _coerce_str_list(repo_types) if repo_types is not None else []
        except ValueError as e:
            return _helper_error(start_calls=start_calls, source=f"/api/collections/{cid}", error=e, collection_id=cid)
        if raw_repo_types:
            allowed_repo_types = set()
            for raw in raw_repo_types:
                canonical = _canonical_repo_type(raw, default="")
                if canonical not in {"model", "dataset", "space"}:
                    return _helper_error(
                        start_calls=start_calls,
                        source=f"/api/collections/{cid}",
                        error=f"Unsupported repo_type '{raw}'",
                        collection_id=cid,
                    )
                allowed_repo_types.add(canonical)

        endpoint = f"/api/collections/{cid}"
        resp = _host_raw_call(endpoint)
        if not resp.get("ok"):
            return _helper_error(
                start_calls=start_calls,
                source=endpoint,
                error=resp.get("error") or "collection fetch failed",
                collection_id=cid,
            )

        payload = resp.get("data") if isinstance(resp.get("data"), dict) else {}
        raw_items = payload.get("items") if isinstance(payload.get("items"), list) else []
        owner = _author_from_any(payload.get("owner"))
        owner_payload = payload.get("owner") if isinstance(payload.get("owner"), dict) else {}
        if owner is None and "/" in cid:
            owner = cid.split("/", 1)[0]

        normalized: list[dict[str, Any]] = []
        for row in raw_items:
            if not isinstance(row, dict):
                continue
            item = _normalize_collection_repo_item(row)
            if item is None:
                continue
            repo_type = item.get("repo_type")
            if allowed_repo_types is not None and repo_type not in allowed_repo_types:
                continue
            if not _item_matches_where(item, where):
                continue
            normalized.append(item)

        total_matched = len(normalized)
        items = [] if count_only else normalized[:lim]
        items = _project_repo_items(items, fields)
        truncated = lim > 0 and total_matched > lim

        return _helper_success(
            start_calls=start_calls,
            source=endpoint,
            items=items,
            scanned=len(raw_items),
            matched=total_matched,
            returned=len(items),
            total=len(raw_items),
            total_matched=total_matched,
            total_population=len(raw_items),
            truncated=truncated,
            complete=not truncated,
            collection_id=cid,
            title=payload.get("title"),
            owner=owner,
            owner_type=owner_payload.get("type") if isinstance(owner_payload.get("type"), str) else None,
            repo_types=sorted(allowed_repo_types) if allowed_repo_types is not None else None,
        )

    async def hf_runtime_capabilities(section: str | None = None) -> dict[str, Any]:
        start_calls = call_count["n"]
        internal_helper_used["used"] = True

        def _render_annotation(annotation: Any) -> str:
            if annotation is inspect.Signature.empty:
                return "Any"
            return str(annotation)

        def _render_default(default: Any) -> str | None:
            if default is inspect.Signature.empty:
                return None
            return repr(default)

        def _signature_payload(fn: Callable[..., Any]) -> dict[str, Any]:
            signature = inspect.signature(fn)
            parameters: list[dict[str, Any]] = []
            for parameter in signature.parameters.values():
                item: dict[str, Any] = {
                    "name": parameter.name,
                    "kind": str(parameter.kind).replace("Parameter.", "").lower(),
                    "annotation": _render_annotation(parameter.annotation),
                    "required": parameter.default is inspect.Signature.empty,
                }
                default = _render_default(parameter.default)
                if default is not None:
                    item["default"] = default
                parameters.append(item)
            return {
                "parameters": parameters,
                "returns": _render_annotation(signature.return_annotation),
            }

        helper_payload = {
            name: _signature_payload(fn)
            for name, fn in sorted(helper_functions.items())
        }

        manifest: dict[str, Any] = {
            "overview": {
                "helper_count": len(helper_functions),
                "supports_current_user": True,
                "supports_raw_api_fallback": True,
                "helper_result_envelope": {
                    "ok": "bool",
                    "item": "dict | None",
                    "items": "list[dict]",
                    "meta": "dict",
                    "error": "str | None",
                },
                "raw_result_envelope": {
                    "result": "Any",
                    "meta": {
                        "ok": "bool",
                        "api_calls": "int",
                        "elapsed_ms": "int",
                        "limits_reached": "bool",
                        "limit_summary": "list[dict]",
                    },
                },
            },
            "helpers": helper_payload,
            "fields": {
                "profile": list(PROFILE_CANONICAL_FIELDS),
                "repo": list(REPO_CANONICAL_FIELDS),
                "user": list(USER_CANONICAL_FIELDS),
                "actor": list(ACTOR_CANONICAL_FIELDS),
                "activity": list(ACTIVITY_CANONICAL_FIELDS),
                "collection": list(COLLECTION_CANONICAL_FIELDS),
            },
            "aliases": {
                "repo": dict(sorted(_REPO_FIELD_ALIASES.items())),
                "user": dict(sorted(_USER_FIELD_ALIASES.items())),
                "actor": dict(sorted(_ACTOR_FIELD_ALIASES.items())),
                "collection": dict(sorted(_COLLECTION_FIELD_ALIASES.items())),
                "sort_keys": dict(sorted(_SORT_KEY_ALIASES.items())),
            },
            "limits": {
                "default_timeout_sec": DEFAULT_TIMEOUT_SEC,
                "default_max_calls": DEFAULT_MAX_CALLS,
                "max_calls_limit": MAX_CALLS_LIMIT,
                "output_items_truncation_limit": OUTPUT_ITEMS_TRUNCATION_LIMIT,
                "graph_scan_limit_cap": GRAPH_SCAN_LIMIT_CAP,
                "likes_scan_limit_cap": LIKES_SCAN_LIMIT_CAP,
                "recent_activity_scan_max_pages": RECENT_ACTIVITY_SCAN_MAX_PAGES,
                "trending_endpoint_max_limit": TRENDING_ENDPOINT_MAX_LIMIT,
                "pagination_policy": {
                    helper_name: dict(sorted(policy.items()))
                    for helper_name, policy in sorted(PAGINATION_POLICY.items())
                },
            },
            "raw_api": {
                "call_api": _signature_payload(call_api),
                "allowed_methods": ["GET", "POST"],
                "allowed_endpoint_patterns": list(ALLOWLIST_PATTERNS),
                "helper_covered_endpoint_patterns": [
                    {"pattern": pattern, "helper": helper_name}
                    for pattern, helper_name in HELPER_COVERED_ENDPOINT_PATTERNS
                ],
            },
            "repo_search": {
                "sort_keys": {
                    repo_type: sorted(keys)
                    for repo_type, keys in sorted(_REPO_SORT_KEYS.items())
                },
                "extra_args": {
                    repo_type: sorted(args)
                    for repo_type, args in sorted(_REPO_SEARCH_EXTRA_ARGS.items())
                },
            },
        }

        allowed_sections = sorted(manifest)
        requested = str(section or "").strip().lower()
        if requested:
            if requested not in manifest:
                return _helper_error(
                    start_calls=start_calls,
                    source="internal://runtime-capabilities",
                    error=f"Unsupported section {section!r}. Allowed sections: {allowed_sections}",
                    section=section,
                    allowed_sections=allowed_sections,
                )
            payload = {
                "section": requested,
                "content": manifest[requested],
                "allowed_sections": allowed_sections,
            }
        else:
            payload = {
                "allowed_sections": allowed_sections,
                **manifest,
            }

        return _helper_success(
            start_calls=start_calls,
            source="internal://runtime-capabilities",
            items=[payload],
            section=requested or None,
        )

    m = pydantic_monty.Monty(
        code,
        inputs=["query", "max_calls"],
        script_name="monty_agent.py",
        type_check=False,
    )

    def _collecting_wrapper(helper_name: str, fn: Callable[..., Any]) -> Callable[..., Any]:
        async def wrapped(*args: Any, **kwargs: Any) -> Any:
            result = await fn(*args, **kwargs)
            summary = _summarize_limit_hit(helper_name, result)
            if summary is not None and len(limit_summaries) < 20:
                limit_summaries.append(summary)
            return result

        return wrapped

    limits: pydantic_monty.ResourceLimits = {
        "max_duration_secs": float(timeout_sec),
        "max_memory": DEFAULT_MONTY_MAX_MEMORY,
        "max_allocations": DEFAULT_MONTY_MAX_ALLOCATIONS,
        "max_recursion_depth": DEFAULT_MONTY_MAX_RECURSION_DEPTH,
    }

    helper_functions = _resolve_helper_functions(locals())

    try:
        result = await pydantic_monty.run_monty_async(
            m,
            inputs={"query": query, "max_calls": max_calls},
            external_functions={
                "call_api": call_api,
                **{name: _collecting_wrapper(name, fn) for name, fn in helper_functions.items()},
            },
            limits=limits,
        )
    except Exception as e:
        raise MontyExecutionError(str(e), call_count["n"], trace) from e

    if call_count["n"] == 0:
        # Some current-user helpers can fail before any live API call is made
        # (for example when request-scoped auth is unavailable). If generated
        # code either returns that explicit helper error envelope or flattens it
        # into an empty fallback shape, preserve the helper-owned error instead
        # of replacing it with a generic zero-call runtime failure.
        if internal_helper_used["used"]:
            return {"output": _truncate_result_payload(result), "api_calls": call_count["n"], "trace": trace, "limit_summaries": limit_summaries}
        if isinstance(result, dict) and result.get("ok") is True:
            meta = result.get("meta") if isinstance(result.get("meta"), dict) else {}
            source = meta.get("source")
            if isinstance(source, str) and source.startswith("internal://"):
                return {"output": _truncate_result_payload(result), "api_calls": call_count["n"], "trace": trace, "limit_summaries": limit_summaries}
        if latest_helper_error is not None:
            return {"output": _truncate_result_payload(latest_helper_error), "api_calls": call_count["n"], "trace": trace, "limit_summaries": limit_summaries}
        if isinstance(result, dict) and result.get("ok") is False and isinstance(result.get("error"), str):
            return {"output": _truncate_result_payload(result), "api_calls": call_count["n"], "trace": trace, "limit_summaries": limit_summaries}
        raise MontyExecutionError("Code completed without calling any external API function", call_count["n"], trace)

    if not any(step.get("ok") is True for step in trace):
        # Allow explicit helper/live failure envelopes to be returned as-is.
        # This preserves concrete API error context (e.g. repo not found) while
        # still blocking fabricated successful fallback outputs.
        if isinstance(result, dict) and result.get("ok") is False and isinstance(result.get("error"), str):
            return {"output": _truncate_result_payload(result), "api_calls": call_count["n"], "trace": trace, "limit_summaries": limit_summaries}
        raise MontyExecutionError(
            "Code completed without a successful API call; refusing non-live fallback result",
            call_count["n"],
            trace,
        )

    return {"output": _truncate_result_payload(result), "api_calls": call_count["n"], "trace": trace, "limit_summaries": limit_summaries}


async def hf_hub_query(
    query: str,
    code: str,
    max_calls: int = DEFAULT_MAX_CALLS,
    timeout_sec: int = DEFAULT_TIMEOUT_SEC,
) -> dict[str, Any]:
    """Use natural-language queries to explore the Hugging Face Hub.

    Best for read-only Hub discovery, lookup, ranking, and relationship questions
    across users, organizations, repositories, activity, followers, likes,
    discussions, and collections.
    """
    if not query or not query.strip():
        raise ValueError("query is required")
    if not code or not code.strip():
        raise ValueError("code is required")

    max_calls = max(1, min(int(max_calls), MAX_CALLS_LIMIT))
    code = code.strip()
    try:
        _validate_generated_code(code)

        run = await _run_with_monty(
            code=code,
            query=query,
            max_calls=max_calls,
            strict_mode=INTERNAL_STRICT_MODE,
            timeout_sec=timeout_sec,
        )
        return {
            "ok": True,
            "data": run["output"],
            "error": None,
            "api_calls": run["api_calls"],
        }
    except MontyExecutionError as e:
        return {
            "ok": False,
            "data": None,
            "error": str(e),
            "api_calls": e.api_calls,
        }
    except Exception as e:
        return {
            "ok": False,
            "data": None,
            "error": str(e),
            "api_calls": 0,
        }


async def hf_hub_query_raw(
    query: str,
    code: str,
    max_calls: int = DEFAULT_MAX_CALLS,
    timeout_sec: int = DEFAULT_TIMEOUT_SEC,
) -> Any:
    """Use natural-language queries to explore the Hugging Face Hub in raw mode.

    Best for read-only Hub discovery, lookup, ranking, and relationship
    questions when the caller wants a runtime-owned raw envelope:
    ``result`` contains the direct ``solve(...)`` output and ``meta`` contains
    execution details such as timing, call counts, and limit summaries.
    """
    if not query or not query.strip():
        raise ValueError("query is required")
    if not code or not code.strip():
        raise ValueError("code is required")

    max_calls = max(1, min(int(max_calls), MAX_CALLS_LIMIT))
    code = code.strip()
    started = time.perf_counter()
    try:
        _validate_generated_code(code)

        run = await _run_with_monty(
            code=code,
            query=query,
            max_calls=max_calls,
            strict_mode=INTERNAL_STRICT_MODE,
            timeout_sec=timeout_sec,
        )
        elapsed_ms = int((time.perf_counter() - started) * 1000)
        return _wrap_raw_result(
            run["output"],
            ok=True,
            api_calls=run["api_calls"],
            elapsed_ms=elapsed_ms,
            limit_summaries=run.get("limit_summaries"),
        )
    except MontyExecutionError as e:
        elapsed_ms = int((time.perf_counter() - started) * 1000)
        return _wrap_raw_result(
            None,
            ok=False,
            api_calls=e.api_calls,
            elapsed_ms=elapsed_ms,
            error=str(e),
        )
    except Exception as e:
        elapsed_ms = int((time.perf_counter() - started) * 1000)
        return _wrap_raw_result(
            None,
            ok=False,
            api_calls=0,
            elapsed_ms=elapsed_ms,
            error=str(e),
        )

def _arg_parser() -> argparse.ArgumentParser:
    p = argparse.ArgumentParser(description="Monty-backed API chaining tool (v2)")
    p.add_argument("--query", required=True, help="Natural language query")
    p.add_argument("--code", default=None, help="Inline Monty code to execute")
    p.add_argument("--code-file", default=None, help="Path to .py file with Monty code to execute")
    p.add_argument("--max-calls", type=int, default=DEFAULT_MAX_CALLS, help="Max external API/helper calls")
    p.add_argument("--timeout", type=int, default=DEFAULT_TIMEOUT_SEC)
    return p


def main() -> int:
    args = _arg_parser().parse_args()
    code = args.code
    if args.code_file:
        with open(args.code_file, "r", encoding="utf-8") as f:
            code = f.read()

    if not code:
        print(json.dumps({"ok": False, "error": "Either --code or --code-file is required"}, ensure_ascii=False))
        return 1

    try:
        out = asyncio.run(
            hf_hub_query(
                query=args.query,
                code=code,
                max_calls=args.max_calls,
                timeout_sec=args.timeout,
            )
        )
        print(json.dumps(out, ensure_ascii=False))
        return 0 if out.get("ok") else 1
    except Exception as e:
        print(json.dumps({"ok": False, "error": str(e)}, ensure_ascii=False))
        return 1


if __name__ == "__main__":
    raise SystemExit(main())