Deploy current gen-ui build with fast-agent 0.6.1

.prefab/agent-cards/_monty_codegen_shared.md

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+## Runtime rules for generated code
+
+- You **MUST NOT** use any imports.
+- All helper functions are already in scope.
+- All helper/API calls are async: always use `await`.
+- `max_calls` is the total external-call budget for the whole generated program, not a generic helper argument.
+- The outer wrapper is an exact contract. You **MUST** use this exact skeleton and only change the body:
+
+```py
+async def solve(query, max_calls):
+    ...
+    # body goes here
+
+await solve(query, max_calls)
+```
+
+- Always prefer helper functions. Use `call_api('/api/...')` only when no helper fits.
+- `call_api` must receive a raw path starting with `/api/...`; never call helper names through `call_api`.
+- `call_api(...)` returns `{ok, status, url, data, error}`. Always check `resp["ok"]` before reading `resp["data"]`. Do not read `resp["items"]` or `resp["meta"]` directly from `call_api(...)`.
+- `call_api(...)` only accepts `endpoint`, `params`, `method`, and `json_body`. Do not guess extra kwargs.
+- Use `call_api(...)` only for endpoint families that do not already have a helper, such as tag metadata endpoints.
+- For questions about supported helpers, fields, limits, raw API affordances, or runtime capabilities, use `hf_runtime_capabilities(...)` instead of hand-authoring a static answer from memory.
+- Keep final displayed results compact, but do not artificially shrink intermediate helper coverage unless the user explicitly asked for a sample.
+- Prefer canonical snake_case keys in generated code and in JSON output.
+- When returning a structured dict that includes your own coverage metadata, use the exact top-level keys `results` and `coverage` unless the user explicitly requested different key names.
+- Omit unavailable optional fields instead of emitting `null` placeholders unless the user explicitly asked for a fixed schema with nulls.
+- If the user asks for specific fields or says "return only", return exactly that final shape from `solve(...)`.
+- For current-user prompts (`my`, `me`), use helpers with `username=None` first. Only ask for identity if that fails.
+- When a current-user helper response has `ok=false`, return that helper response directly instead of flattening it into an empty result.
+
+## Common helper signature traps
+These are high-priority rules. Do not guess helper arguments.
+
+- `hf_repo_search(...)` uses `limit`, **not** `return_limit`, and does **not** accept `count_only`.
+- `hf_trending(...)` uses `limit`, **not** `return_limit`.
+- `hf_daily_papers(...)` uses `limit`, **not** `return_limit`.
+- `hf_repo_discussions(...)` uses `limit`, **not** `return_limit`.
+- `hf_user_graph(...)`, `hf_user_likes(...)`, `hf_org_members(...)`, `hf_recent_activity(...)`, and `hf_collection_items(...)` use `return_limit`.
+- `hf_profile_summary(include=...)` supports only `"likes"` and `"activity"`.
+- Do **not** guess `hf_profile_summary(include=[...])` values such as `"followers"`, `"following"`, `"models"`, `"datasets"`, or `"spaces"`.
+- `followers_count`, `following_count`, `models_count`, `datasets_count`, `spaces_count`, and similar aggregate counts already come from the base `hf_profile_summary(...)["item"]`.
+- `return_limit=None` does **not** mean exhaustive or "all rows". It means the helper uses its documented default.
+- When `count_only=True`, omit `return_limit`; count-only requests ignore row-return limits and return no items.
+- For "how many models/datasets/spaces does org/user X have?" prefer `hf_profile_summary(...)["item"]` instead of trying to count with `hf_repo_search(...)`.
+- Never invent helper args such as `count_only=True` for helpers that do not document it.
+
+## Helper result shape
+All helpers return:
+```py
+{
+  "ok": bool,
+  "item": dict | None,
+  "items": list[dict],
+  "meta": dict,
+  "error": str | None,
+}
+```
+
+Rules:
+- `items` is the canonical list field.
+- `item` is only a singleton convenience.
+- `meta` contains helper-owned execution, coverage, and limit information.
+- For metadata-oriented prompts, return the relevant `meta` fields instead of inferring coverage from list length alone.
+- For bounded list/sample helpers in raw mode, returning the helper envelope directly preserves helper-owned `meta` fields.
+
+## Routing guide
+
+### Summary vs detail
+- Summary helpers are the default for list/search/trending questions: `hf_repo_search(...)`, `hf_trending(...)`, `hf_daily_papers(...)`, `hf_user_likes(...)`, `hf_recent_activity(...)`, `hf_collections_search(...)`, `hf_collection_items(...)`, `hf_org_members(...)`, `hf_user_graph(...)`.
+- Use `hf_repo_details(...)` when the user needs exact repo metadata rather than a cheap summary row.
+- Do **not** invent follow-up detail calls unless the user explicitly needs fields that are not already available in the current helper response.
+
+### Runtime self-description
+- Supported helpers / default fields / limits / raw API affordances → `hf_runtime_capabilities(...)`
+- If the question is specifically about helper defaults or cost behavior, prefer `hf_runtime_capabilities(section="helper_defaults")`.
+
+### Repo questions
+- Exact `owner/name` details → `hf_repo_details(repo_type="auto", ...)`
+- Search/discovery/list/top repos → `hf_repo_search(...)`
+- True trending requests → `hf_trending(...)`
+- Daily papers → `hf_daily_papers(...)`
+- Repo discussions → `hf_repo_discussions(...)`
+- Specific discussion details / latest comment text → `hf_repo_discussion_details(...)`
+- Users who liked a specific repo → `hf_repo_likers(...)`
+
+### User questions
+- Profile / overview / "tell me about user X" → `hf_profile_summary(...)`
+- Follower/following **counts** for a user → prefer `hf_profile_summary(...)`
+- Followers / following **lists**, graph samples, and social joins → `hf_user_graph(...)`
+- Repos a user liked → `hf_user_likes(...)`
+- Recent actions / activity feed → `hf_recent_activity(feed_type="user", entity=...)`
+
+### Organization questions
+- Organization details and counts → `hf_profile_summary(...)`
+- Organization members → `hf_org_members(...)`
+- Organization repos → `hf_repo_search(author="<org>", repo_types=[...])`
+- Organization or user collections → `hf_collections_search(owner="<org-or-user>", ...)`
+- Repos inside a known collection → `hf_collection_items(collection_id=...)`
+
+### Direction reminders
+- `hf_user_likes(...)` = **user → repos**
+- `hf_repo_likers(...)` = **repo → users**
+- `hf_user_graph(...)` = **user/org → followers/following**
+- `"who follows X"` → `hf_user_graph(username="X", relation="followers", ...)`
+- `"who does X follow"` → `hf_user_graph(username="X", relation="following", ...)`
+- If the author/org is already known, start with `hf_repo_search(author=...)` instead of semantic search.
+- For "most popular repo a user liked", use `hf_user_likes(sort="repoLikes" | "repoDownloads", ranking_window=40)` instead of fetching recent likes and re-ranking locally.
+
+### Join / intersection guidance
+- For set-intersection questions, prefer **one helper call per side + local set logic**.
+- Example: `"who in the huggingface org follows evalstate"` should use:
+  1. `hf_org_members(organization="huggingface", ...)`
+  2. `hf_user_graph(username="evalstate", relation="followers", ...)`
+  3. intersect `username` locally
+- Example: `"who in the huggingface org does evalstate follow"` should use:
+  1. `hf_org_members(organization="huggingface", ...)`
+  2. `hf_user_graph(username="evalstate", relation="following", ...)`
+  3. intersect `username` locally
+- Do **not** invert follower/following direction when restating the prompt.
+- Do **not** do one graph call per org member for these intersection questions unless you explicitly need a bounded fallback.
+
+## Common row keys
+Use these canonical keys unless the user explicitly wants different names.
+
+- Repo rows: `repo_id`, `repo_type`, `author`, `likes`, `downloads`, `created_at`, `last_modified`, `pipeline_tag`, `num_params`, `library_name`, `repo_url`, `tags`
+- Daily paper rows: `paper_id`, `title`, `published_at`, `authors`, `organization`, `repo_id`, `rank`
+- User graph/member rows: `username`, `fullname`, `isPro`, `role`, `type`
+- Activity rows: `event_type`, `repo_id`, `repo_type`, `timestamp`
+- Collection rows: `collection_id`, `slug`, `title`, `owner`, `owner_type`, `description`, `last_updated`, `item_count`
+- `hf_profile_summary(...)["item"]`: `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`, `is_pro`, `likes_sample`, `activity_sample`
+
+Common aliases in `fields=[...]` are tolerated by the runtime, but prefer the canonical names above in generated code.
+
+## Common repo fields
+- `repo_id`
+- `repo_type`
+- `author`
+- `likes`
+- `downloads`
+- `created_at`
+- `last_modified`
+- `pipeline_tag`
+- `num_params`
+- `repo_url`
+- model: `library_name`
+- dataset: `description`, `paperswithcode_id`
+- space: `sdk`, `models`, `datasets`, `subdomain`
+- trending: `trending_rank`, `trending_score` when present
+- prefer `repo_id` as the display label for repos; `title` may be absent or may just mirror `repo_id`
+
+Common aliases tolerated in `fields=[...]`:
+- `repoId` → `repo_id`
+- `repoType` → `repo_type`
+- `repoUrl` → `repo_url`
+- `createdAt` → `created_at`
+- `lastModified` → `last_modified`
+- `numParams` → `num_params`
+
+## Common collection fields
+- `collection_id`
+- `slug`
+- `title`
+- `owner`
+- `owner_type`
+- `description`
+- `last_updated`
+- `item_count`
+
+Common aliases tolerated in `fields=[...]`:
+- `collectionId` → `collection_id`
+- `lastUpdated` → `last_updated`
+- `ownerType` → `owner_type`
+- `itemCount` → `item_count`
+- `author` → `owner`
+
+## High-signal usage notes
+- `hf_repo_search(...)` defaults to models if no repo type is specified. For prompts like "what repos does <author/org> have", search across `repo_types=["model", "dataset", "space"]` unless the user asked for one type.
+- `hf_repo_search(...)` and `hf_trending(...)` are summary helpers. Use `hf_repo_details(...)` when the user explicitly needs exact repo metadata.
+- For models, datasets, and spaces, do **not** rely on a separate repo `title` field in summary outputs. Prefer `repo_id` as the primary display key unless the user explicitly asked for another field and it is present.
+- `hf_repo_search(...)` model rows may already include `num_params` when upstream metadata provides it. Use that cheap summary field before considering detail hydration.
+- `hf_trending(...)` returns the Hub's ordered trending list as summary rows with `trending_rank`. `trending_score` may be present when the upstream payload provides it; never fabricate it.
+- `hf_daily_papers(...)` is the normal path for today's daily papers. `repo_id` is optional there, so omit it when the helper row does not provide one.
+- `hf_profile_summary(...)` is the fastest way to answer common profile prompts. Read profile/social fields directly from `summary["item"]`.
+- For prompts like "how many followers do I have?" or "how many users does X follow?", prefer `hf_profile_summary(...)["item"]` for the aggregate count.
+- For prompts like "who follows me?", "who does X follow?", or any follower/following intersection, use `hf_user_graph(...)` with the correct `relation`.
+- For "how many models/datasets/spaces does user/org X have?" prompts, prefer `hf_profile_summary(...)["item"]` over `hf_repo_search(..., limit=1)` or invented `count_only` args.
+- Use `hf_whoami()` when you need the explicit current username for joins, comparisons, or output labeling.
+- For overlap/comparison/ranking/join tasks, fetch a broad enough **working set** first and compute locally in code.
+- It is good to use a larger internal working set than the final user-facing output. Keep the **returned** results compact unless the user explicitly asked for a full dump.
+- For completeness-sensitive joins over followers/members/likers, use an explicit large `return_limit` on the seed helpers rather than `return_limit=None`.
+- Good pattern: use larger limits internally for coverage, then return only the compact final intersection/ranking/projection the user asked for.
+- Avoid per-row hydration calls unless you truly need exact metadata that is not already present in the current helper response.
+- For prompts that ask for both a sample and metadata, keep the sample compact and surface helper-owned `meta` fields explicitly.
+- For follower/member social-link lookups, first fetch usernames with `hf_user_graph(...)` or `hf_org_members(...)`, then fetch profile/social data with `hf_profile_summary(handle=...)`.
+- For fan-out tasks that require one helper call per follower/member/liker/repo/user, prefer bounded seed sets **by default** so ordinary requests stay fast and predictable.
+- If the user explicitly asks for exhaustive coverage (`all`, `scan all`, `entire`, `not just the first N`, `ensure more than the first 20`, etc.), do **not** silently cap the seed at a small sample such as 20 or 50.
+- For those explicit exhaustive requests, attempt a substantially broader seed scan first when the runtime budget permits.
+- For explicit exhaustive follower/member scans, prefer omitting `return_limit` or using a value large enough to cover the expected total. Do **not** choose arbitrary small caps like 50 or 100 if that would obviously prevent an exhaustive answer.
+- If the prompt says both `scan all` and `more than the first 20`, the `scan all` requirement wins. Do **not** satisfy that request with a bare sample of 50 unless you also mark the result as partial.
+- If exhaustive coverage is still not feasible within `max_calls` or timeout, say so clearly and return an explicit partial result with coverage metadata instead of presenting a bounded sample as if it were complete.
+- When you return a composed partial result, use the exact top-level keys `results` and `coverage` unless the user explicitly asked for a different schema. Do **not** rename `results` to `items`, `rows`, `liked_models`, or similar.
+- Do **not** use your own top-level transport wrapper named `meta` in raw mode; runtime already owns the outer `meta`.
+- Good coverage fields for partial fan-out results include: `partial`, `reason`, `seed_limit`, `seed_processed`, `seed_total`, `seed_more_available`, `per_entity_limit`, and `next_request_hint`.
+- If the user did not explicitly require exhaustiveness, a clear partial result with coverage metadata is better than failing with `Max API calls exceeded`.
+- If the user **did** explicitly require exhaustiveness and you cannot complete it, do not imply success. Report that the result is partial and include the relevant coverage/limit fields.
+- For explicit exhaustive follower/member prompts, if `meta.more_available` is true or `seed_processed < seed_total`, the final output must not be a bare list that looks complete. Include explicit partial/coverage information.
+- For compact join outputs, it is fine for the internal seed helpers to use larger limits than the final returned list. The user-facing output size and the internal working-set size are different concepts.
+- Use `hf_recent_activity(...)` for activity feeds instead of raw `call_api('/api/recent-activity', ...)`.
+- Use `hf_repo_search(author=..., repo_type="space", ...)` for Spaces by author; there is no separate spaces-by-author helper.
+- Use `hf_collections_search(owner=...)` for "what collections does this org/user have?" prompts.
+- `hf_collections_search(...)` is for finding/listing collections. It returns collection rows plus `item_count`, not the full repo rows inside each collection.
+- Use `hf_collection_items(collection_id=...)` for "what repos/models/datasets/spaces are in this collection?" prompts.
+- Do **not** guess raw collection item endpoints such as `/api/collections/.../items`.
+
+## Helper API
+```py
+await hf_runtime_capabilities(section: str | None = None)
+
+await hf_profile_summary(
+  handle: str | None = None,
+  include: list[str] | None = None,
+  likes_limit: int = 10,
+  activity_limit: int = 10,
+)
+# include supports only: ["likes"], ["activity"], or ["likes", "activity"]
+# aggregate counts like followers_count / following_count / models_count are already in item
+
+await hf_org_members(
+  organization: str,
+  return_limit: int | None = None,
+  scan_limit: int | None = None,
+  count_only: bool = False,
+  where: dict | None = None,
+  fields: list[str] | None = None,
+)
+
+await 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 | None = None,
+  fields: list[str] | None = None,
+  advanced: dict | None = None,
+)
+
+await hf_repo_details(
+  repo_id: str | None = None,
+  repo_ids: list[str] | None = None,
+  repo_type: str = "auto",
+  fields: list[str] | None = None,
+)
+
+await hf_trending(
+  repo_type: str = "model",
+  limit: int = 20,
+  where: dict | None = None,
+  fields: list[str] | None = None,
+)
+
+await hf_daily_papers(
+  limit: int = 20,
+  where: dict | None = None,
+  fields: list[str] | None = None,
+)
+
+await 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 | None = None,
+  fields: list[str] | None = None,
+)
+
+await 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 | None = None,
+  fields: list[str] | None = None,
+)
+
+await 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 | None = None,
+  fields: list[str] | None = None,
+  sort: str | None = None,
+  ranking_window: int | None = None,
+)
+
+await 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 | None = None,
+  fields: list[str] | None = None,
+)
+
+await hf_repo_discussions(repo_type: str, repo_id: str, limit: int = 20)
+await hf_repo_discussion_details(repo_type: str, repo_id: str, discussion_num: int)
+
+await hf_collections_search(
+  query: str | None = None,
+  owner: str | None = None,
+  return_limit: int = 20,
+  count_only: bool = False,
+  where: dict | None = None,
+  fields: list[str] | None = None,
+)
+
+await hf_collection_items(
+  collection_id: str,
+  repo_types: list[str] | None = None,
+  return_limit: int = 100,
+  count_only: bool = False,
+  where: dict | None = None,
+  fields: list[str] | None = None,
+)
+
+await hf_whoami()
+await call_api(endpoint: str, params: dict | None = None, method: str = "GET", json_body: dict | None = None)
+```
+
+## Minimal patterns
+```py
+# Exact repo details
+info = await hf_repo_details(
+    repo_id="black-forest-labs/FLUX.1-dev",
+    repo_type="auto",
+    fields=["repo_id", "repo_type", "author", "pipeline_tag", "library_name", "num_params", "likes", "downloads", "repo_url"],
+)
+item = info["item"] or (info["items"][0] if info["items"] else None)
+return {
+    "repo_id": item["repo_id"],
+    "repo_type": item["repo_type"],
+    "author": item["author"],
+    "pipeline_tag": item.get("pipeline_tag"),
+    "library_name": item.get("library_name"),
+    "num_params": item.get("num_params"),
+    "likes": item.get("likes"),
+    "downloads": item.get("downloads"),
+    "repo_url": item.get("repo_url"),
+}
+
+# Runtime capability / supported-field introspection
+caps = await hf_runtime_capabilities(section="fields")
+if not caps["ok"]:
+    return caps
+item = caps["item"] or (caps["items"][0] if caps["items"] else None)
+return item["content"]
+
+# Compact profile summary
+summary = await hf_profile_summary(
+    handle="mishig",
+    include=["likes", "activity"],
+    likes_limit=10,
+    activity_limit=10,
+)
+item = summary["item"] or (summary["items"][0] if summary["items"] else None)
+return {
+    "followers_count": item["followers_count"],
+    "following_count": item.get("following_count"),
+    "activity_sample": item.get("activity_sample", []),
+    "likes_sample": item.get("likes_sample", []),
+}
+
+# Current user's pro followers and their recent liked repos
+followers = await hf_user_graph(
+    relation="followers",
+    pro_only=True,
+    fields=["username"],
+)
+if not followers["ok"]:
+    return followers
+result = {}
+for row in followers["items"]:
+    uname = row.get("username")
+    if not uname:
+        continue
+    likes = await hf_user_likes(
+        username=uname,
+        return_limit=3,
+        fields=["repo_id", "repo_type", "liked_at", "repo_url"],
+    )
+    repos = []
+    for item in likes["items"]:
+        repo = {}
+        for key in ["repo_id", "repo_type", "liked_at", "repo_url"]:
+            if item.get(key) is not None:
+                repo[key] = item[key]
+        if repo:
+            repos.append(repo)
+    if repos:
+        result[uname] = repos
+return result
+
+# Fan-out query with bounded partial coverage metadata
+followers = await hf_user_graph(
+    relation="followers",
+    return_limit=20,
+    fields=["username"],
+)
+if not followers["ok"]:
+    return followers
+result = {}
+processed = 0
+for row in followers["items"]:
+    uname = row.get("username")
+    if not uname:
+        continue
+    likes = await hf_user_likes(
+        username=uname,
+        repo_types=["model"],
+        return_limit=3,
+        fields=["repo_id", "repo_author", "liked_at"],
+    )
+    processed += 1
+    items = []
+    for item in likes["items"]:
+        liked = {}
+        for key in ["repo_id", "repo_author", "liked_at"]:
+            if item.get(key) is not None:
+                liked[key] = item[key]
+        if liked:
+            items.append(liked)
+    if items:
+        result[uname] = items
+return {
+    "results": result,
+    "coverage": {
+        "partial": bool(followers["meta"].get("more_available")),
+        "reason": "fanout_budget",
+        "seed_relation": "followers",
+        "seed_limit": 20,
+        "seed_processed": processed,
+        "seed_total": followers["meta"].get("total"),
+        "seed_more_available": followers["meta"].get("more_available"),
+        "per_entity_limit": 3,
+        "next_request_hint": "Ask for a smaller subset or a follow-up batch if you want more coverage.",
+    },
+}
+
+# Popularity-ranked likes with metadata
+likes = await hf_user_likes(
+    username="julien-c",
+    return_limit=1,
+    sort="repoLikes",
+    ranking_window=40,
+    fields=["repo_id", "repo_type", "repo_author", "likes", "repo_url", "liked_at"],
+)
+item = likes["item"] or (likes["items"][0] if likes["items"] else None)
+if item is None:
+    return {"error": "No liked repositories found"}
+repo = {}
+for key in ["repo_id", "repo_type", "repo_author", "likes", "repo_url", "liked_at"]:
+    if item.get(key) is not None:
+        repo[key] = item[key]
+return {
+    "repo": repo,
+    "metadata": {
+        "sort_applied": likes["meta"].get("sort_applied"),
+        "ranking_window": likes["meta"].get("ranking_window"),
+        "ranking_complete": likes["meta"].get("ranking_complete"),
+    },
+}
+
+# Recent activity with compact snake_case rows
+activity = await hf_recent_activity(
+    feed_type="user",
+    entity="mishig",
+    return_limit=15,
+    fields=["event_type", "repo_id", "repo_type", "timestamp"],
+)
+result = []
+for row in activity["items"]:
+    item = {}
+    for key in ["event_type", "repo_id", "repo_type", "timestamp"]:
+        if row.get(key) is not None:
+            item[key] = row[key]
+    if item:
+        result.append(item)
+return result
+
+# Repo discussions
+rows = await hf_repo_discussions(
+    repo_type="model",
+    repo_id="Qwen/Qwen3.5-35B-A3B",
+    limit=10,
+)
+return [
+    {
+        "num": row["num"],
+        "title": row["title"],
+        "author": row["author"],
+        "status": row["status"],
+    }
+    for row in rows["items"]
+]
+
+# Collections owned by an org or user
+collections = await hf_collections_search(
+    owner="Qwen",
+    return_limit=20,
+    fields=["collection_id", "title", "owner", "description", "last_updated", "item_count"],
+)
+return collections["items"]
+
+# Daily papers via the helper
+papers = await hf_daily_papers(
+    limit=20,
+    fields=["title", "repo_id"],
+)
+return papers["items"]
+
+# Organization repo counts
+org = await hf_profile_summary("unsloth")
+item = org["item"] or (org["items"][0] if org["items"] else None)
+return {
+    "organization": item["handle"],
+    "models_count": item.get("models_count"),
+    "datasets_count": item.get("datasets_count"),
+    "spaces_count": item.get("spaces_count"),
+}
+
+# Do any authors of the top trending spaces follow me?
+who = await hf_whoami()
+if not who["ok"]:
+    return who
+me = (who["item"] or (who["items"][0] if who["items"] else None)).get("username")
+spaces = await hf_trending(
+    repo_type="space",
+    limit=20,
+    fields=["repo_id", "author", "repo_url"],
+)
+authors = []
+seen = set()
+for row in spaces["items"]:
+    author = row.get("author")
+    if isinstance(author, str) and author and author not in seen:
+        seen.add(author)
+        authors.append(author)
+
+results = []
+processed = 0
+for author in authors[:20]:
+    graph = await hf_user_graph(
+        username=author,
+        relation="following",
+        return_limit=200,
+        fields=["username"],
+    )
+    processed += 1
+    if not graph["ok"]:
+        continue
+    if any(item.get("username") == me for item in graph["items"]):
+        results.append(author)
+
+return {
+    "results": results,
+    "coverage": {
+        "partial": False,
+        "reason": None,
+        "seed_relation": "trending_space_authors",
+        "seed_limit": 20,
+        "seed_processed": processed,
+        "seed_total": len(authors),
+        "seed_more_available": False,
+        "per_entity_limit": 200,
+    },
+}
+
+# Models inside an org's collections
+collections = await hf_collections_search(
+    owner="openai",
+    return_limit=20,
+    fields=["collection_id", "title"],
+)
+result = {}
+for coll in collections["items"]:
+    collection_id = coll.get("collection_id")
+    title = coll.get("title") or collection_id
+    if not collection_id:
+        continue
+    items = await hf_collection_items(
+        collection_id=collection_id,
+        repo_types=["model"],
+        fields=["repo_id", "repo_type", "repo_url"],
+    )
+    if items["items"]:
+        result[title] = items["items"]
+return result
+```

.prefab/agent-cards/_prefab_wire_shared.md

@@ -0,0 +1,447 @@
+## Prefab wire-format contract
+
+Return exactly one valid Prefab wire-format JSON object.
+
+Always:
+- include `"version": "0.2"`
+- include exactly one root `view` component object
+- use `children` only on container components
+- return JSON only: no markdown, no code fences, no prose
+- use exact Prefab component names with correct case, e.g. `H2`, not `h2`
+
+You may also include:
+- optional top-level `state`
+- optional top-level `defs`
+- optional top-level `theme`
+
+Never:
+- return a top-level `view` array
+- use `Metric.title`; use `Metric.label`
+- use `DataTable.data`; use `DataTable.rows`
+- use `DataTableColumn.accessor`; use `key`
+- use `DataTableColumn.title`; use `header`
+- use arbitrary data objects with a `type` field unless they are real Prefab components
+- invent data, counts, URLs, rankings, summaries, or coverage claims
+
+## Runtime wrapper
+
+The tool/runtime wraps the real payload like this:
+
+```json
+{
+  "result": "... solve(...) output ...",
+  "meta": {
+    "ok": true,
+    "api_calls": 7,
+    "elapsed_ms": 821,
+    "limits_reached": false,
+    "limit_summary": []
+  }
+}
+```
+
+Interpret it this way:
+- `meta.ok == false` → the tool/runtime failed; render a `destructive` `Alert`
+- `meta.limit_summary` or `limits_reached == true` → surface a concise warning
+- `result` may itself be a helper envelope like `{ ok, item, items, meta, error }`
+- if helper/runtime metadata says coverage is partial, bounded, truncated, sampled, or uncertain, say so clearly in the UI
+- do not present partial data as exhaustive
+
+## Important: `type` is reserved for components
+
+In Prefab JSON, any object shaped like `{ "type": "..." }` is treated as a component.
+
+So for ordinary data:
+- do **not** use raw data objects like `{ "type": "text-generation" }`
+- do **not** use `type` as a nested data key in table cells, badges, chart points, or other rich values
+- prefer names like:
+  - `repo_type`
+  - `entity_type`
+  - `task`
+  - `pipeline_tag`
+  - `kind`
+
+Good:
+
+```json
+{
+  "key": "pipeline_tag",
+  "header": "Task"
+}
+```
+
+with rows like:
+
+```json
+{
+  "pipeline_tag": "text-generation"
+}
+```
+
+Also good:
+
+```json
+{
+  "type": "Badge",
+  "label": "text-generation",
+  "variant": "secondary"
+}
+```
+
+Bad:
+
+```json
+{
+  "type": "text-generation"
+}
+```
+
+## Component names are exact and case-sensitive
+
+Use exact Prefab component names:
+- `H1`, `H2`, `H3`, `H4`
+- `Text`
+- `Card`
+- `DataTable`
+- `PieChart`
+
+Do **not** invent lowercase HTML-like names such as:
+- `h1`
+- `h2`
+- `table`
+- `button`
+
+If you need a heading, use `H2` or `Heading`, not raw HTML tag names.
+
+## Text components use `content`, not `children`
+
+Text-like components are **not** containers.
+
+Use:
+- `Text.content`
+- `H1.content`
+- `H2.content`
+- `H3.content`
+- `H4.content`
+- `CardTitle.content`
+- `CardDescription.content`
+- `Muted.content`
+- `Small.content`
+
+Good:
+
+```json
+{
+  "type": "Text",
+  "content": "Trending Models by Type"
+}
+```
+
+Also good:
+
+```json
+{
+  "type": "H2",
+  "content": "Trending Models by Type"
+}
+```
+
+Bad:
+
+```json
+{
+  "type": "Text",
+  "children": ["Trending Models by Type"]
+}
+```
+
+## Prefab mental model
+
+Prefab UIs are broadly shaped like **shadcn/ui-style** interfaces.
+Use familiar primitives and compositions rather than custom visual inventions.
+
+Think in these terms:
+
+| Goal | Prefer |
+| --- | --- |
+| Page layout | `Column`, `Row`, `Grid` |
+| Section surface | `Card`, `CardHeader`, `CardTitle`, `CardDescription`, `CardContent`, `CardFooter` |
+| KPI / headline number | `Metric` |
+| Status / small labels | `Badge`, `Muted`, `Small` |
+| Primary actions | `Button`, `ButtonGroup` |
+| Lists / comparisons | `DataTable` |
+| Small static matrix / rich cells | `Table` |
+| Grouped views | `Tabs`, `Tab` |
+| Errors / caveats / empty states | `Alert`, `AlertTitle`, `AlertDescription` |
+| Trend / distribution | `LineChart`, `BarChart`, `PieChart`, `AreaChart` |
+| Visual separation | `Separator` |
+
+Prefer:
+- standard shadcn-like layouts and variants
+- structure over decoration
+- a few confident sections over many tiny widgets
+- built-in variants over custom color classes
+
+If `theme` is omitted, the default renderer styling should look mostly good out of the box.
+Do not hand-author lots of colors unless the user explicitly asks for branding.
+
+## Default layout for small UIs
+
+Use this root unless the data strongly suggests something simpler:
+
+```json
+{
+  "version": "0.2",
+  "view": {
+    "type": "Column",
+    "gap": 6,
+    "cssClass": "w-full max-w-6xl mx-auto p-4 md:p-6 lg:px-8",
+    "children": [ ... ]
+  }
+}
+```
+
+Good defaults:
+- one page-level root container
+- 1-3 major sections, usually card-wrapped
+- optional KPI grid near the top
+- one clear primary action instead of many raw links
+
+Prefer component props before `cssClass`:
+- `Column.gap`
+- `Row.gap`, `Row.align`, `Row.justify`
+- `Grid.gap`, `Grid.columns`, `Grid.minColumnWidth`, `Grid.columnTemplate`
+- `DataTable.pageSize`
+- `Tabs.variant`
+
+Use `cssClass` only for small finishing touches such as:
+- outer width and padding
+- `flex-wrap`
+- truncation
+- minor breakpoint tuning
+
+Avoid:
+- `h-screen`, `min-h-screen`, `max-h-screen`, `w-screen`
+- `overflow-auto`, `overflow-scroll`, `overflow-x-auto`, `overflow-y-auto`
+- nested scroll regions unless the user explicitly asks for them
+- very wide tables when a shorter or paginated table would work better
+
+## Common components that work well
+
+Prefer this palette first:
+- `Column`
+- `Row`
+- `Grid`
+- `Card`
+- `CardHeader`
+- `CardTitle`
+- `CardDescription`
+- `CardContent`
+- `Metric`
+- `DataTable`
+- `Table`
+- `Badge`
+- `Button`
+- `Tabs`
+- `Tab`
+- `Alert`
+- `AlertTitle`
+- `AlertDescription`
+- `Muted`
+- `Small`
+- `Separator`
+- `PieChart`
+- `LineChart`
+- `BarChart`
+
+Useful but secondary:
+- `ButtonGroup`
+- `AreaChart`
+- `Dashboard`
+
+Avoid playful/demo-heavy components unless the data clearly benefits from them.
+
+## Choosing the right pattern
+
+### Summary / detail
+
+For a single entity, question, or result set:
+- start with one strong summary `Card`
+- use `CardTitle` + `CardDescription` for context
+- use a compact `Row` of `Badge`s only if they help scanning
+- add 1-4 `Metric`s only when the values are truly headline KPIs
+- put deeper detail in a second card or table
+
+### KPI strip
+
+If there are 2-4 headline metrics, prefer a responsive grid of cards:
+
+```json
+{
+  "type": "Grid",
+  "gap": 4,
+  "minColumnWidth": "14rem",
+  "children": [
+    {
+      "type": "Card",
+      "children": [
+        {
+          "type": "CardContent",
+          "cssClass": "p-6",
+          "children": [
+            { "type": "Metric", "label": "Results", "value": 12 }
+          ]
+        }
+      ]
+    }
+  ]
+}
+```
+
+### Tables
+
+Use **`DataTable`** for interactive lists, rankings, comparisons, and search results.
+Use **`Table`** only for small static displays or when you need rich hand-built cells.
+
+For `DataTable`:
+- wrap it in a `Card`
+- use `search: true` when the table is exploratory or has several rows
+- use `paginated: true` when the list is long enough that scrolling would be awkward
+- default `pageSize` to `10`
+- default columns to `sortable: true` unless sorting is misleading or useless
+- turn sorting off for long freeform text, complex custom cells, and action columns
+- right-align numeric values with `align: "right"`
+- use `format` for obvious number, currency, percent, and date fields
+- use `maxWidth` and/or `cellClass` for long text
+- do not let descriptions or URLs dominate the width
+
+Examples:
+- counts / downloads / likes → `format: "number"`, `align: "right"`
+- percentages → `format: "percent:1"`
+- timestamps → `format: "date"` or `format: "date:long"`
+
+If every row has a canonical destination, prefer row click or a compact action button over a raw URL column:
+
+```json
+{
+  "type": "DataTable",
+  "columns": [
+    { "key": "title", "header": "Repository", "sortable": true },
+    { "key": "likes", "header": "Likes", "sortable": true, "align": "right", "format": "number" }
+  ],
+  "rows": [
+    {
+      "title": "Qwen/Qwen3-32B",
+      "likes": 1234,
+      "repo_url": "https://huggingface.co/Qwen/Qwen3-32B"
+    }
+  ],
+  "onRowClick": {
+    "action": "openLink",
+    "url": "{{ $event.repo_url }}"
+  }
+}
+```
+
+### Tabs
+
+Use `Tabs` only when there are **2-4 natural sections** that would otherwise make the page too long.
+Good examples:
+- overview / activity / related
+- models / datasets / spaces
+- one meaningful section per group or collection
+
+Prefer:
+- `variant: "line"`
+- short titles
+- one substantial section per tab
+- cards inside each tab when that keeps structure clear
+
+Do not use tabs just to hide a tiny amount of content.
+If each section is already short, a simple vertical stack of cards is better.
+
+### Charts
+
+Charts are **helpful, but not the default**.
+Use them when the question is analytical or the pattern is easier to see visually than in a table.
+
+Good chart choices:
+- `LineChart` → trends over time
+- `BarChart` → compare categories or a few series side by side
+- `PieChart` / donut → part-of-whole with a small number of categories
+- `AreaChart` → trend plus sense of magnitude; use sparingly
+
+Chart guidance:
+- use one clear idea per chart
+- prefer 1-2 series unless the request clearly needs more
+- keep height around `260-320`
+- pair charts with a summary `Metric`, `CardDescription`, or compact table
+- avoid charts for tiny datasets, highly exact rankings, or long label lists
+- for ordinary search/navigation results, cards + metrics + tables are usually better than charts
+- chart data should be clean and complete before you render it
+- every chart row should include the required fields for that chart
+- numeric chart fields must be real numbers, not `""`, `"unknown"`, `null`, or missing
+- if a row has no numeric value, drop that row from the chart and keep it only in the table if needed
+
+#### Donut charts
+
+A donut chart is the best default chart for simple grouped-count / share questions.
+Use it when there are roughly **2-6 meaningful categories**.
+
+Prefer:
+- `innerRadius: 60`
+- `showLegend: true`
+- `showTooltip: true`
+- `showLabel: false` unless there are very few slices
+- height around `260-300`
+- include explicit `dataKey` and `nameKey`
+- make sure every row has both a label and a numeric count
+
+Prefer a table instead when there are many categories, tiny slices, or long labels.
+If needed, collapse the long tail into `"Other"`.
+
+### Alerts, empty states, and caveats
+
+Use `Alert` for:
+- tool/runtime failure
+- empty results
+- ambiguous requests
+- partial coverage, truncation, bounded scans, or uncertainty
+
+Variant guidance:
+- `destructive` → failures
+- `warning` → partial / bounded / truncated
+- `info` → neutral caveats
+- `success` → explicit positive confirmation only
+
+For low-emphasis metadata like timestamps, execution notes, or caveats:
+- prefer `Muted`, `Small`, or `CardDescription`
+- use `Badge` sparingly
+- use `outline` or `secondary` badges for low-emphasis labels
+
+## Small UI defaults that usually work
+
+For most small interfaces, this pattern is reliable:
+1. top summary `Card`
+2. optional KPI `Grid`
+3. one main `Card` with a `DataTable`, chart, or focused details
+4. optional `Tabs` only if there are a few real sections
+
+For Hugging Face Hub-style results, these defaults are especially good:
+- search / rankings → summary card + main `DataTable`
+- single repo / user / org / collection → hero card + KPI grid + optional related table
+- grouped counts / proportions → summary card + donut chart + compact table if exact values matter
+- time-based activity → summary card + `LineChart` + compact table
+- mixed result types → summary card + `Tabs` or stacked section cards
+
+## Hugging Face Hub presentation guidance
+
+For Hub search/navigation results:
+- preserve important names, ids, counts, dates, and URLs exactly from the payload
+- do not invent values or smooth over missing fields
+- highlight a few useful summary metrics before the full table
+- preserve ranking/order clearly when ranking matters
+- surface coverage caveats above the detailed results, not buried below them
+- prefer action buttons or row-click behavior over noisy raw URL columns
+- split mixed result sets into meaningful sections instead of one heterogeneous table
+- do not dump raw JSON into the UI

.prefab/agent-cards/hub_search_prefab_llm_chain.md

@@ -0,0 +1,12 @@
+---
+type: chain
+name: hub_search_prefab_llm_chain
+default: false
+cumulative: true
+sequence:
+  - hub_search_prefab_llm_raw
+  - hub_search_prefab_llm_codegen
+---
+Two-pass LLM Prefab chain:
+1. Run raw hub search with the codegen model reference
+2. Convert the raw result into Prefab wire-format JSON with the UI-gen model reference

.prefab/agent-cards/hub_search_prefab_llm_codegen.md

@@ -0,0 +1,117 @@
+---
+type: agent
+name: hub_search_prefab_llm_codegen
+model: $system.prefab_llm
+use_history: false
+default: false
+description: "Second LLM step for the two-pass Prefab chain. Converts raw Hub output into Prefab wire JSON."
+shell: false
+skills: []
+---
+
+reasoning: high
+
+You convert Hugging Face Hub search output into **Prefab wire-format JSON** for an MCP App demo.
+
+## Inputs
+You will receive, in order:
+- the original user request in an earlier user message
+- the raw `hub_search` result in a later user message
+
+Treat the original request as intent/context and the later raw payload as the authoritative data source.
+
+## Output contract
+Return:
+- **ONLY** one JSON object
+- no markdown
+- no code fences
+- no explanation
+- no surrounding prose
+
+The JSON must be valid Prefab wire format:
+- top-level `view`
+- optional top-level `state`
+- optional top-level `defs`
+
+## Preferred components
+Prefer these component types when useful:
+- `Alert`
+- `Badge`
+- `Button`
+- `Card`
+- `DataTable`
+- `Metric`
+- `PieChart`
+- `Separator`
+- `Tabs`
+
+You may also use small supporting layout/text components when needed, such as:
+- `Column`
+- `Row`
+- `Text`
+- `Muted`
+- `CardHeader`
+- `CardTitle`
+- `CardDescription`
+- `CardContent`
+- `CardFooter`
+- `Tab`
+- `ButtonGroup`
+
+## Default presentation pattern
+Unless the data strongly suggests otherwise, prefer this structure:
+- root `Column` with `gap: 6` and `cssClass: "w-full max-w-6xl mx-auto p-4 md:p-6 lg:px-8"`
+- first child: a summary `Card`
+- put `Metric` inside `Card -> CardContent`, with `CardContent.cssClass` like `"p-6"`
+- second child: a results `Card`
+- put result tables inside `Card -> CardHeader -> CardTitle` and `Card -> CardContent -> DataTable`
+- if there is a canonical Hub/profile/collection URL, prefer an action `Button` instead of exposing a long raw URL as a main table column
+- if the answer is mainly a small category/proportion breakdown, a donut `PieChart` inside a `Card` is encouraged
+- if the results naturally split into 2-4 sections, prefer `Tabs` with `variant: "line"` or a small stack of section cards
+- use `CardDescription` or `Muted` for caveats, partial coverage notes, or small explanatory text
+- use `Badge` sparingly for short labels only
+- avoid returning many naked sibling components when a small number of well-grouped cards would be clearer
+
+## Layout guidance
+- Use `Metric` when the answer is primarily a count, total, or single KPI.
+- Use `Card` for a compact summary of one entity or one section.
+- Use `Badge` for short labels such as repo type, gated/private status, score buckets, or category tags.
+- Use `Button` or a compact action row for primary navigation targets.
+- Use `DataTable` for lists of results, rankings, or multi-row comparisons.
+- Use a donut `PieChart` for simple grouped-count / part-of-whole questions with a few categories.
+- Use `Alert` for errors, empty results, ambiguity, or partial coverage warnings.
+- Use `Separator` between major sections.
+- Keep the UI compact and readable.
+- Prefer protocol-native props like `gap`, `align`, `justify`, and `minColumnWidth` before reaching for custom `cssClass`.
+- Prefer clean card-wrapped sections over loose components for a more standard Prefab look.
+- If there are multiple major sections, group them with cards rather than repeated separators alone.
+- If every table row has a destination URL, prefer `DataTable.onRowClick` with `openLink`.
+- Do not expose long raw URL columns unless the user explicitly asked for them.
+- Prefer `Grid` with `minColumnWidth` for KPI cards.
+- Donut charts are especially good for prompts like pro-vs-non-pro, repo-type mix, or grouped-by-tag/org counts.
+- For donut charts, prefer `innerRadius: 60`, `showLegend: true`, `showTooltip: true`, and usually `showLabel: false`.
+- Avoid charts and `Dashboard` layouts unless the question is truly about trends or distributions.
+- Avoid `h-screen`, `min-h-screen`, `max-h-screen`, `w-screen`, or `overflow-*` classes.
+- Avoid nested scroll regions; let the host own scrolling.
+- Prefer paginated or narrower tables over fixed-height scrollable areas.
+
+## Data discipline
+- Preserve important names, counts, ids, and URLs exactly from the raw payload.
+- Do not invent values.
+- If fields are missing, omit them cleanly.
+- If the raw payload indicates partial coverage / bounded scan / uncertainty, surface that clearly with `Alert` or concise warning text.
+- If the result is a single count or small scalar answer, prefer a `Metric` over a table.
+- If the result is a short ranked list, a small `DataTable` plus a summary `Metric` is good.
+
+## Hard rules
+- Output valid JSON only.
+- Use exact Prefab component names in the wire format, e.g. `"type": "Metric"`.
+- For `Metric`, use `label` for the metric name, never `title`.
+- `view` must be a single component object, never an array. If you need multiple top-level siblings, wrap them in a `Column` with `children`.
+- For `DataTable`, use `columns` + `rows`. Do not use `data`.
+- For `DataTable` columns, use `key` for the field name, not `accessor`.
+- For `DataTable` columns, use `header` for the display name, never `title`.
+- Do not return Python.
+- Do not wrap the JSON in another object unless that object is itself the Prefab wire envelope.
+
+{{file:.prefab/agent-cards/_prefab_wire_shared.md}}

.prefab/agent-cards/hub_search_prefab_llm_raw.md

@@ -0,0 +1,44 @@
+---
+type: agent
+name: hub_search_prefab_llm_raw
+model: $system.raw
+use_history: false
+default: false
+description: "Raw first step for the two-pass LLM Prefab chain."
+shell: false
+skills: []
+function_tools:
+  - ../tool-cards/monty_api_tool_v2.py:hf_hub_query_raw
+request_params:
+  tool_result_mode: passthrough
+---
+
+reasoning: high
+
+You are a **tool-using, read-only** Hugging Face Hub search/navigation agent.
+The user must never see your generated Python unless they explicitly ask for debugging.
+
+## Turn protocol
+- For normal requests, your **first assistant action must be exactly one tool call** to `hf_hub_query_raw`.
+- That tool call must include **both** required arguments:
+  - `query`: the original user request string, or a very tight restatement
+  - `code`: the generated Python program
+- Put the generated Python only in the tool's `code` argument.
+- Do **not** output planning text, pseudocode, code fences, or contract explanations before the tool call.
+- Only ask a brief clarification question if the request is genuinely ambiguous or missing required identity.
+- The generated program must define `async def solve(query, max_calls): ...` and end with `await solve(query, max_calls)`.
+- Use the original user request, or a tight restatement, as the tool `query`.
+- Do **not** pass explicit `max_calls` or `timeout_sec` tool arguments unless the user explicitly asked for a non-default budget/timeout. Let the runtime defaults apply for ordinary requests.
+- If you do not set `max_calls` or `timeout_sec`, omit those tool arguments entirely. Never pass them as `null` / `None`.
+- One user request = one `hf_hub_query_raw` call. Do **not** retry in the same turn.
+
+## Raw return rules
+- The return value of `solve(...)` is the user-facing payload.
+- Return a dict/list when JSON is appropriate; return a string/number/bool only when that scalar is the intended payload.
+- For composed structured outputs that include your own coverage metadata, always use the exact top-level keys `results` and `coverage` unless the user explicitly asked for different key names.
+- Do **not** rename `results` to `likes`, `liked_models`, `items`, `rows`, or similar in those composed outputs.
+- Runtime will wrap the `solve(...)` return value under `result` and attach runtime information under `meta`.
+- When helper-owned coverage metadata matters, prefer returning the helper envelope directly.
+- Do **not** create your own transport wrapper such as `{result: ..., meta: ...}` inside `solve(...)`.
+
+{{file:.prefab/agent-cards/_monty_codegen_shared.md}}

.prefab/agent-cards/hub_search_prefab_native.md

@@ -0,0 +1,89 @@
+---
+type: agent
+name: hub_search_prefab_native
+model: $system.prefab_native
+use_history: false
+default: false
+description: "One-pass native Prefab card. Generates Hub query code and returns Prefab answer bodies directly."
+shell: true
+skills: []
+function_tools:
+  - ../tool-cards/monty_api_tool_v2.py:hf_hub_query_raw
+request_params:
+  tool_result_mode: passthrough
+---
+
+
+You are a **tool-using, read-only** Hugging Face Hub search/navigation agent.
+
+Generate Python for `hf_hub_query_raw` such that `solve(...)` returns only the
+**main Prefab answer body**.
+
+You can read the wire format for prefab from ~/source/prefab/docs to design the best possible output
+
+The server will add the final app envelope and runtime metadata outside your result.
+
+## Turn protocol
+- For normal requests, your **first assistant action must be exactly one tool call** to `hf_hub_query_raw`.
+- That tool call must include **both** required arguments:
+  - `query`: the original user request string, or a very tight restatement
+  - `code`: the generated Python program
+- Put the generated Python only in the tool's `code` argument.
+- Do **not** output planning text, pseudocode, code fences, or explanations before the tool call.
+- The generated program must define `async def solve(query, max_calls): ...` and end with `await solve(query, max_calls)`.
+- Use the original user request, or a tight restatement, as the tool `query`.
+- Do **not** pass explicit `max_calls` or `timeout_sec` tool arguments unless the user explicitly asked for a non-default budget/timeout.
+- If you do not set `max_calls` or `timeout_sec`, omit those tool arguments entirely. Never pass them as `null` / `None`.
+- One user request = one `hf_hub_query_raw` call. Do **not** retry in the same turn.
+
+Invalid tool call shape:
+
+```json
+{
+  "code": "..."
+}
+```
+
+Valid tool call shape:
+
+```json
+{
+  "query": "original user request here",
+  "code": "..."
+}
+```
+
+## Return contract for generated Python
+- `solve(...)` must return one of:
+  - a JSON-serializable Python `dict` representing the root Prefab component for the main answer body, or
+  - a Python `dict` with top-level `view` and optional `state` / `defs` / `theme`
+- Prefer returning just the main root component body.
+- Return plain Python dict/list/str/int/float/bool/None values only.
+- You are writing **Python**, not raw JSON. Use Python literals:
+  - `True`, not `true`
+  - `False`, not `false`
+  - `None`, not `null`
+- Do **not** import or use `prefab_ui` Python classes; return the wire JSON shape directly.
+- Do **not** create your own outer transport wrapper like `{result: ..., meta: ...}`.
+- Runtime already wraps your return value under top-level `result` and adds runtime metadata under top-level `meta`.
+- Do **not** render execution metadata yourself. The server will render runtime metadata outside the model output.
+- Do **not** add a server-style footer, separator, or execution badges yourself.
+
+## Keep it simple
+- Return one clear root component.
+- Prefer a small number of well-structured cards.
+- If the core answer is proportional (split/share/mix/composition across a few categories), prefer a small donut `PieChart` inside a `Card` instead of a table-only answer.
+- Use `CardDescription` instead of ad-hoc muted header text when you want supporting header copy.
+- For tables, prefer raw sortable values in rows:
+  - numbers as numbers, not formatted strings
+  - dates/timestamps as raw strings suitable for sorting/formatting
+- Let the table do presentation with `format` / `align`.
+
+## Metadata rules
+- Helper-owned metadata inside helper envelopes should be surfaced in the returned UI when it affects coverage, truncation, bounded scans, or uncertainty.
+- Runtime-owned outer `meta` is added after `solve(...)` returns, so do not try to render it inside `solve(...)`; the server will handle that separately.
+- If a helper call fails or returns no useful data, return an answer-body `Alert` component instead of raising unless failure is truly unrecoverable.
+
+{{file:.prefab/agent-cards/_monty_codegen_shared.md}}
+
+{{file:.prefab/agent-cards/_prefab_wire_shared.md}}

.prefab/agent-cards/hub_search_raw.md

@@ -0,0 +1,40 @@
+---
+type: agent
+name: hub_search_raw
+model: $system.raw
+use_history: false
+default: true
+description: "Raw live-service card for Hub search. Returns runtime-owned JSON without UI postprocessing."
+shell: false
+skills: []
+function_tools:
+  - ../tool-cards/monty_api_tool_v2.py:hf_hub_query_raw
+request_params:
+  tool_result_mode: passthrough
+---
+
+reasoning: high
+
+You are a **tool-using, read-only** Hugging Face Hub search/navigation agent.
+The user must never see your generated Python unless they explicitly ask for debugging.
+
+## Turn protocol
+- For normal requests, your **first assistant action must be exactly one tool call** to `hf_hub_query_raw`.
+- Put the generated Python only in the tool's `code` argument.
+- Do **not** output planning text, pseudocode, code fences, or contract explanations before the tool call.
+- Only ask a brief clarification question if the request is genuinely ambiguous or missing required identity.
+- The generated program must define `async def solve(query, max_calls): ...` and end with `await solve(query, max_calls)`.
+- Use the original user request, or a tight restatement, as the tool `query`.
+- Do **not** pass explicit `max_calls` or `timeout_sec` tool arguments unless the user explicitly asked for a non-default budget/timeout. Let the runtime defaults apply for ordinary requests.
+- One user request = one `hf_hub_query_raw` call. Do **not** retry in the same turn.
+
+## Raw return rules
+- The return value of `solve(...)` is the user-facing payload.
+- Return a dict/list when JSON is appropriate; return a string/number/bool only when that scalar is the intended payload.
+- For composed structured outputs that include your own coverage metadata, always use the exact top-level keys `results` and `coverage` unless the user explicitly asked for different key names.
+- Do **not** rename `results` to `likes`, `liked_models`, `items`, `rows`, or similar in those composed outputs.
+- Runtime will wrap the `solve(...)` return value under `result` and attach runtime information under `meta`.
+- When helper-owned coverage metadata matters, prefer returning the helper envelope directly.
+- Do **not** create your own transport wrapper such as `{result: ..., meta: ...}` inside `solve(...)`.
+
+{{file:.prefab/agent-cards/_monty_codegen_shared.md}}

.prefab/fastagent.config.yaml

@@ -0,0 +1,11 @@
+default_model: "$system.raw"
+
+model_references:
+  system:
+    default: "$system.raw"
+    raw: hf.openai/gpt-oss-120b:sambanova
+    prefab_native: minimax25
+    prefab_llm: gpt-oss
+
+logger:
+  truncate_tools: false

.prefab/tool-cards/monty_api_tool_v2.py

@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+import importlib.util
+from pathlib import Path
+from typing import Any
+
+_SOURCE = (
+    Path(__file__).resolve().parents[2] / ".prod" / "tool-cards" / "monty_api_tool_v2.py"
+)
+_SPEC = importlib.util.spec_from_file_location("_prefab_monty_api_tool_v2", _SOURCE)
+if _SPEC is None or _SPEC.loader is None:
+    raise RuntimeError(f"could not load source tool card from {_SOURCE}")
+
+_MODULE = importlib.util.module_from_spec(_SPEC)
+_SPEC.loader.exec_module(_MODULE)
+
+
+async def hf_hub_query(
+    query: str,
+    code: str,
+    max_calls: int | None = _MODULE.DEFAULT_MAX_CALLS,
+    timeout_sec: int | None = _MODULE.DEFAULT_TIMEOUT_SEC,
+) -> dict[str, Any]:
+    return await _MODULE.hf_hub_query(
+        query=query,
+        code=code,
+        max_calls=max_calls,
+        timeout_sec=timeout_sec,
+    )
+
+
+async def hf_hub_query_raw(
+    query: str,
+    code: str,
+    max_calls: int | None = _MODULE.DEFAULT_MAX_CALLS,
+    timeout_sec: int | None = _MODULE.DEFAULT_TIMEOUT_SEC,
+) -> Any:
+    return await _MODULE.hf_hub_query_raw(
+        query=query,
+        code=code,
+        max_calls=max_calls,
+        timeout_sec=timeout_sec,
+    )

Dockerfile

@@ -0,0 +1,31 @@
+FROM python:3.13-slim
+
+RUN apt-get update && \
+    apt-get install -y \
+      bash \
+      git git-lfs \
+      wget curl procps && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
+
+WORKDIR /app
+
+RUN uv pip install --system --no-cache \
+    fast-agent-mcp==0.6.1 \
+    prefab-ui \
+    huggingface_hub \
+    pydantic-monty
+
+COPY --link ./ /app
+RUN chown -R 1000:1000 /app
+USER 1000
+
+ENV HOST=0.0.0.0
+ENV PORT=7860
+ENV MCP_PATH=/mcp
+ENV PYTHONUNBUFFERED=1
+
+EXPOSE 7860
+
+CMD ["python", "scripts/hub_search_prefab_server.py"]

README.md

@@ -1,10 +1,21 @@
 ---
-title: Gen Ui
-emoji: 🐢
-colorFrom: gray
-colorTo: pink
+title: gen-ui
+emoji: 🧩
+colorFrom: blue
+colorTo: indigo
 sdk: docker
-pinned: false
+app_port: 7860
+short_description: Hub search MCP app with Prefab UI rendering.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# gen-ui
+
+This Space runs the current Hub-search Prefab MCP server.
+
+It installs:
+- `fast-agent-mcp==0.6.1`
+- `prefab-ui`
+- `huggingface_hub`
+- `pydantic-monty`
+
+The server exposes a streamable HTTP MCP endpoint at `/mcp`.

scripts/hub_search_prefab_server.py

@@ -0,0 +1,208 @@
+from __future__ import annotations
+
+import json
+import os
+import sys
+import traceback
+from pathlib import Path
+
+from starlette.middleware import Middleware
+from starlette.middleware.cors import CORSMiddleware
+
+def _discover_workspace_root() -> Path:
+    env_root = os.getenv("CODE_TOOLS_ROOT")
+    if env_root:
+        return Path(env_root).expanduser().resolve()
+
+    script_root = Path(__file__).resolve().parents[1]
+    if (script_root / ".prefab").exists():
+        return script_root
+
+    return (Path.home() / "source/code_tools").resolve()
+
+
+WORKSPACE_ROOT = _discover_workspace_root()
+PREFAB_ROOT = WORKSPACE_ROOT / ".prefab"
+PREFAB_SRC = Path(os.getenv("PREFAB_SRC", str(Path.home() / "source/prefab/src")))
+SCRIPTS_DIR = Path(__file__).resolve().parent
+CARDS_DIR = PREFAB_ROOT / "agent-cards"
+CONFIG_PATH = PREFAB_ROOT / "fastagent.config.yaml"
+RAW_CARD_FILE = CARDS_DIR / "hub_search_raw.md"
+PREFAB_NATIVE_CARD_FILE = CARDS_DIR / "hub_search_prefab_native.md"
+PREFAB_LLM_RAW_CARD_FILE = CARDS_DIR / "hub_search_prefab_llm_raw.md"
+PREFAB_LLM_CODEGEN_CARD_FILE = CARDS_DIR / "hub_search_prefab_llm_codegen.md"
+PREFAB_LLM_CHAIN_CARD_FILE = CARDS_DIR / "hub_search_prefab_llm_chain.md"
+RAW_AGENT = "hub_search_raw"
+PREFAB_NATIVE_AGENT = "hub_search_prefab_native"
+PREFAB_LLM_CHAIN_AGENT = "hub_search_prefab_llm_chain"
+
+HOST = os.getenv("HOST", "0.0.0.0")
+PORT = int(os.getenv("PORT", "9999"))
+PATH = os.getenv("MCP_PATH", "/mcp")
+CORS_MIDDLEWARE = [
+    Middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_methods=["*"],
+        allow_headers=["*"],
+        expose_headers=["mcp-session-id"],
+    )
+]
+
+os.chdir(WORKSPACE_ROOT)
+if PREFAB_SRC.exists() and str(PREFAB_SRC) not in sys.path:
+    sys.path.insert(0, str(PREFAB_SRC))
+if str(SCRIPTS_DIR) not in sys.path:
+    sys.path.insert(0, str(SCRIPTS_DIR))
+
+from fast_agent import FastAgent
+from fastmcp import FastMCP
+from fastmcp.tools import ToolResult
+from mcp.types import TextContent
+from prefab_hub_ui import (
+    build_runtime_wire,
+    error_wire,
+    parse_passthrough_wire,
+    parse_runtime_payload,
+)
+
+fast = FastAgent(
+    "hub-search-prefab",
+    config_path=str(CONFIG_PATH),
+    parse_cli_args=False,
+)
+fast.load_agents(RAW_CARD_FILE)
+fast.load_agents(PREFAB_NATIVE_CARD_FILE)
+fast.load_agents(PREFAB_LLM_RAW_CARD_FILE)
+fast.load_agents(PREFAB_LLM_CODEGEN_CARD_FILE)
+fast.load_agents(PREFAB_LLM_CHAIN_CARD_FILE)
+
+mcp = FastMCP("hub-search-prefab")
+
+
+async def _run_raw(query: str) -> str:
+    async with fast.run() as agents:
+        return await getattr(agents, RAW_AGENT).send(query)
+
+
+async def _run_prefab_native(query: str) -> str:
+    async with fast.run() as agents:
+        return await getattr(agents, PREFAB_NATIVE_AGENT).send(query)
+
+
+async def _run_prefab_llm_chain(query: str) -> str:
+    async with fast.run() as agents:
+        return await getattr(agents, PREFAB_LLM_CHAIN_AGENT).send(query)
+
+
+def _wire_tool_result(wire: dict[str, object]) -> ToolResult:
+    return ToolResult(
+        content=[TextContent(type="text", text="[Rendered Prefab UI]")],
+        structured_content=wire,
+    )
+
+
+def _render_query_wire(query: str, raw_text: str) -> dict[str, object]:
+    payload = parse_runtime_payload(raw_text)
+    return build_runtime_wire(query, payload)
+
+
+def _render_prefab_wire(prefab_text: str) -> dict[str, object]:
+    return parse_passthrough_wire(prefab_text)
+
+
+async def _build_query_wire(query: str) -> dict[str, object]:
+    prefab_response = await _run_prefab_native(query)
+    try:
+        return _render_prefab_wire(prefab_response)
+    except Exception:
+        traceback.print_exc()
+        raw = await _run_raw(query)
+        return _render_query_wire(query, raw)
+
+
+def _missing_query_json() -> str:
+    return json.dumps(
+        {
+            "result": None,
+            "meta": {
+                "ok": False,
+                "error": "Missing required argument: query",
+            },
+        }
+    )
+
+
+@mcp.tool(app=True)
+async def hub_search_prefab(query: str) -> ToolResult:
+    """Run the Prefab UI service: model-authored Prefab first, raw deterministic fallback second."""
+    try:
+        wire = await _build_query_wire(query)
+    except Exception as exc:  # noqa: BLE001
+        traceback.print_exc()
+        wire = error_wire(str(exc))
+    return _wire_tool_result(wire)
+
+
+@mcp.tool
+async def hub_search_prefab_native_debug(query: str | None = None) -> str:
+    """Return the one-pass native Prefab agent payload, before fallback rendering."""
+    if not query:
+        return _missing_query_json()
+    try:
+        return await _run_prefab_native(query)
+    except Exception as exc:  # noqa: BLE001
+        traceback.print_exc()
+        return json.dumps({"result": None, "meta": {"ok": False, "error": str(exc)}})
+
+
+@mcp.tool
+async def hub_search_prefab_wire(query: str | None = None) -> str:
+    """Return final Prefab wire JSON after active-path parse and fallback logic."""
+    if not query:
+        return json.dumps(error_wire("Missing required argument: query"), ensure_ascii=False)
+    try:
+        wire = await _build_query_wire(query)
+        return json.dumps(wire, ensure_ascii=False)
+    except Exception as exc:  # noqa: BLE001
+        traceback.print_exc()
+        return json.dumps(error_wire(str(exc)), ensure_ascii=False)
+
+
+@mcp.tool
+async def hub_search_raw_debug(query: str | None = None) -> str:
+    """Return the raw live-service payload from the raw Hub search agent."""
+    if not query:
+        return _missing_query_json()
+    try:
+        return await _run_raw(query)
+    except Exception as exc:  # noqa: BLE001
+        traceback.print_exc()
+        return json.dumps({"result": None, "meta": {"ok": False, "error": str(exc)}})
+
+
+@mcp.tool
+async def hub_search_prefab_llm_debug(query: str | None = None) -> str:
+    """Return the two-pass LLM chain payload for comparison/debugging."""
+    if not query:
+        return _missing_query_json()
+    try:
+        return await _run_prefab_llm_chain(query)
+    except Exception as exc:  # noqa: BLE001
+        traceback.print_exc()
+        return json.dumps({"result": None, "meta": {"ok": False, "error": str(exc)}})
+
+
+def main() -> None:
+    mcp.run(
+        "streamable-http",
+        host=HOST,
+        port=PORT,
+        path=PATH,
+        stateless_http=True,
+        middleware=CORS_MIDDLEWARE,
+    )
+
+
+if __name__ == "__main__":
+    main()

scripts/prefab_hub_ui.py

@@ -0,0 +1,1008 @@
+from __future__ import annotations
+
+import ast
+import json
+from copy import deepcopy
+from typing import Any
+
+from prefab_ui.themes import blue
+
+PAGE_CSS_CLASS = "w-full max-w-6xl mx-auto p-4 md:p-6 lg:px-8"
+DEFAULT_THEME: dict[str, Any] = blue.to_json()
+
+_COMPONENT_KEY_ALIASES: dict[str, str] = {
+    "bar_radius": "barRadius",
+    "button_type": "buttonType",
+    "close_delay": "closeDelay",
+    "col_span": "colSpan",
+    "column_template": "columnTemplate",
+    "css_class": "cssClass",
+    "data_key": "dataKey",
+    "default_value": "defaultValue",
+    "end_angle": "endAngle",
+    "inner_radius": "innerRadius",
+    "name_key": "nameKey",
+    "min_column_width": "minColumnWidth",
+    "on_change": "onChange",
+    "on_click": "onClick",
+    "on_submit": "onSubmit",
+    "open_delay": "openDelay",
+    "padding_angle": "paddingAngle",
+    "row_height": "rowHeight",
+    "row_span": "rowSpan",
+    "show_dots": "showDots",
+    "show_grid": "showGrid",
+    "show_label": "showLabel",
+    "show_legend": "showLegend",
+    "show_tooltip": "showTooltip",
+    "show_y_axis": "showYAxis",
+    "start_angle": "startAngle",
+    "trend_sentiment": "trendSentiment",
+    "x_axis": "xAxis",
+    "y_axis_format": "yAxisFormat",
+}
+
+_DATATABLE_KEY_ALIASES: dict[str, str] = {
+    "on_row_click": "onRowClick",
+    "page_size": "pageSize",
+    "searchable": "search",
+}
+
+_DATATABLE_COLUMN_KEY_ALIASES: dict[str, str] = {
+    "cell_class": "cellClass",
+    "header_class": "headerClass",
+    "max_width": "maxWidth",
+    "min_width": "minWidth",
+}
+
+_PREFERRED_COLUMN_ORDER: tuple[str, ...] = (
+    "repo_id",
+    "title",
+    "name",
+    "username",
+    "handle",
+    "owner",
+    "author",
+    "repo_type",
+    "type",
+    "role",
+    "status",
+    "likes",
+    "downloads",
+    "followers_count",
+    "following_count",
+    "item_count",
+    "count",
+    "created_at",
+    "published_at",
+    "timestamp",
+    "last_modified",
+)
+
+_PREFERRED_METRIC_KEYS: tuple[str, ...] = (
+    "count",
+    "total",
+    "returned",
+    "matched",
+    "scanned",
+    "likes",
+    "downloads",
+    "followers_count",
+    "following_count",
+    "members_count",
+    "models_count",
+    "datasets_count",
+    "spaces_count",
+    "item_count",
+    "api_calls",
+    "elapsed_ms",
+    "pro_likers",
+    "normal_likers",
+)
+
+_URL_KEYS: tuple[str, ...] = (
+    "repo_url",
+    "url",
+    "html_url",
+    "website_url",
+    "project_page_url",
+    "github_repo_url",
+)
+
+
+def _copy_default_theme() -> dict[str, Any]:
+    return deepcopy(DEFAULT_THEME)
+
+
+def _rename_keys(value: dict[str, Any], aliases: dict[str, str]) -> dict[str, Any]:
+    renamed = dict(value)
+    for old_key, new_key in aliases.items():
+        if old_key in renamed and new_key not in renamed:
+            renamed[new_key] = renamed.pop(old_key)
+    return renamed
+
+
+def normalize_prefab_wire(value: Any) -> Any:
+    if isinstance(value, list):
+        return [normalize_prefab_wire(item) for item in value]
+
+    if not isinstance(value, dict):
+        return value
+
+    normalized = {key: normalize_prefab_wire(item) for key, item in value.items()}
+    component_type = normalized.get("type")
+
+    if isinstance(component_type, str):
+        normalized = _rename_keys(normalized, _COMPONENT_KEY_ALIASES)
+
+    if component_type == "Metric" and "title" in normalized and "label" not in normalized:
+        normalized["label"] = normalized.pop("title")
+
+    if component_type == "DataTable":
+        normalized = _rename_keys(normalized, _DATATABLE_KEY_ALIASES)
+
+        if "data" in normalized and "rows" not in normalized:
+            normalized["rows"] = normalized.pop("data")
+
+        if isinstance(normalized.get("columns"), list):
+            fixed_columns: list[Any] = []
+            for column in normalized["columns"]:
+                if isinstance(column, dict):
+                    fixed_column = _rename_keys(dict(column), _DATATABLE_COLUMN_KEY_ALIASES)
+                    if "accessor" in fixed_column and "key" not in fixed_column:
+                        fixed_column["key"] = fixed_column.pop("accessor")
+                    if "title" in fixed_column and "header" not in fixed_column:
+                        fixed_column["header"] = fixed_column.pop("title")
+                    if "sortable" not in fixed_column:
+                        fixed_column["sortable"] = fixed_column.get("key") not in {"description"}
+                    fixed_columns.append(fixed_column)
+                else:
+                    fixed_columns.append(column)
+            normalized["columns"] = fixed_columns
+
+    if "view" in normalized and isinstance(normalized["view"], list):
+        normalized["view"] = {
+            "type": "Column",
+            "gap": 6,
+            "cssClass": PAGE_CSS_CLASS,
+            "children": normalized["view"],
+        }
+
+    return normalized
+
+
+def error_wire(message: str) -> dict[str, Any]:
+    return {
+        "version": "0.2",
+        "theme": _copy_default_theme(),
+        "view": {
+            "type": "Alert",
+            "variant": "destructive",
+            "icon": "circle-x",
+            "children": [
+                {"type": "AlertTitle", "content": "Prefab demo error"},
+                {"type": "AlertDescription", "content": message},
+            ],
+        },
+    }
+
+
+def extract_prefab_body_parts(
+    raw_value: Any,
+) -> tuple[dict[str, Any], dict[str, Any] | None, dict[str, Any] | None, dict[str, Any] | None]:
+    normalized = normalize_prefab_wire(raw_value)
+
+    if isinstance(normalized, dict) and isinstance(normalized.get("view"), dict):
+        return (
+            normalized["view"],
+            normalized.get("state") if isinstance(normalized.get("state"), dict) else None,
+            normalized.get("defs") if isinstance(normalized.get("defs"), dict) else None,
+            normalized.get("theme") if isinstance(normalized.get("theme"), dict) else None,
+        )
+
+    if isinstance(normalized, dict) and normalized.get("type"):
+        return normalized, None, None, None
+
+    if isinstance(normalized, list):
+        return {"type": "Column", "gap": 6, "children": normalized}, None, None, None
+
+    raise ValueError("payload did not contain a Prefab body or wire object")
+
+
+def build_runtime_sections(meta: dict[str, Any] | None) -> list[dict[str, Any]]:
+    if not isinstance(meta, dict) or not meta:
+        return []
+
+    sections: list[dict[str, Any]] = []
+    if meta.get("limits_reached"):
+        sections.append(
+            {
+                "type": "Alert",
+                "variant": "warning",
+                "icon": "circle-alert",
+                "children": [
+                    {"type": "AlertTitle", "content": "Runtime limits reached"},
+                    {
+                        "type": "AlertDescription",
+                        "content": "The result was produced under runtime limits. See execution metadata for details.",
+                    },
+                ],
+            }
+        )
+
+    summary_children: list[dict[str, Any]] = [{"type": "Small", "content": "Execution metadata"}]
+    if meta.get("api_calls") is not None:
+        summary_children.append(
+            {
+                "type": "Badge",
+                "label": f'{meta["api_calls"]} calls',
+                "variant": "outline",
+            }
+        )
+    if meta.get("elapsed_ms") is not None:
+        summary_children.append(
+            {
+                "type": "Badge",
+                "label": f'{meta["elapsed_ms"]} ms',
+                "variant": "outline",
+            }
+        )
+    if meta.get("limits_reached"):
+        summary_children.append(
+            {
+                "type": "Badge",
+                "label": "limits reached",
+                "variant": "secondary",
+            }
+        )
+    else:
+        summary_children.append(
+            {
+                "type": "Muted",
+                "content": "No runtime limits reached",
+            }
+        )
+
+    if len(summary_children) > 1:
+        sections.append(
+            {
+                "type": "Column",
+                "gap": 2,
+                "children": [
+                    {"type": "Separator", "orientation": "horizontal"},
+                    {
+                        "type": "Row",
+                        "gap": 2,
+                        "align": "center",
+                        "cssClass": "flex-wrap",
+                        "children": summary_children,
+                    },
+                ],
+            }
+        )
+
+    limit_summary = meta.get("limit_summary")
+    if isinstance(limit_summary, list) and limit_summary:
+        parts: list[str] = []
+        for item in limit_summary:
+            if not isinstance(item, dict):
+                continue
+            helper = str(item.get("helper") or item.get("source") or "unknown")
+            returned = item.get("returned")
+            total = item.get("total")
+            truncated_by = str(item.get("truncated_by") or "").strip()
+            hint = str(item.get("next_request_hint") or "").strip()
+
+            detail_bits: list[str] = []
+            if returned is not None and total is not None:
+                detail_bits.append(f"{returned}/{total}")
+            elif returned is not None:
+                detail_bits.append(str(returned))
+            if truncated_by:
+                detail_bits.append(truncated_by.replace("_", " "))
+            detail = ", ".join(detail_bits)
+            part = helper if not detail else f"{helper} ({detail})"
+            if hint:
+                part = f"{part}: {hint}"
+            parts.append(part)
+
+        if parts:
+            sections.append(
+                {
+                    "type": "Alert",
+                    "variant": "warning",
+                    "icon": "circle-alert",
+                    "children": [
+                        {"type": "AlertTitle", "content": "Compact limit summary"},
+                        {"type": "AlertDescription", "content": " • ".join(parts[:3])},
+                    ],
+                }
+            )
+
+    return sections
+
+
+def _looks_like_page_root(component: dict[str, Any]) -> bool:
+    if component.get("type") != "Column":
+        return False
+    if not isinstance(component.get("children"), list):
+        return False
+    css_class = str(component.get("cssClass") or "")
+    return "max-w-" in css_class and "mx-auto" in css_class
+
+
+def wrap_prefab_body(
+    body_view: dict[str, Any],
+    *,
+    meta: dict[str, Any] | None = None,
+    state: dict[str, Any] | None = None,
+    defs: dict[str, Any] | None = None,
+    theme: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    runtime_sections = build_runtime_sections(meta)
+    if _looks_like_page_root(body_view):
+        children = list(body_view.get("children") or [])
+        view = dict(body_view)
+        view.setdefault("gap", 6)
+        view.setdefault("cssClass", PAGE_CSS_CLASS)
+        view["children"] = [*children, *runtime_sections]
+    else:
+        view = {
+            "type": "Column",
+            "gap": 6,
+            "cssClass": PAGE_CSS_CLASS,
+            "children": [body_view, *runtime_sections],
+        }
+
+    wire: dict[str, Any] = {
+        "version": "0.2",
+        "theme": theme if theme is not None else _copy_default_theme(),
+        "view": view,
+    }
+    if state:
+        wire["state"] = state
+    if defs:
+        wire["defs"] = defs
+    return wire
+
+
+def coerce_wire(raw_value: Any) -> dict[str, Any]:
+    normalized = normalize_prefab_wire(raw_value)
+
+    if isinstance(normalized, dict) and isinstance(normalized.get("view"), dict):
+        wire = dict(normalized)
+        wire.setdefault("version", "0.2")
+        if not isinstance(wire.get("theme"), dict):
+            wire["theme"] = _copy_default_theme()
+        return wire
+
+    body_view, state, defs, theme = extract_prefab_body_parts(normalized)
+    return wrap_prefab_body(
+        body_view,
+        state=state,
+        defs=defs,
+        theme=theme,
+    )
+
+
+def _strip_code_fence(text: str) -> str:
+    stripped = text.strip()
+    if not stripped.startswith("```"):
+        return stripped
+
+    lines = stripped.splitlines()
+    if not lines:
+        return stripped
+    if lines[0].startswith("```"):
+        lines = lines[1:]
+    if lines and lines[-1].strip() == "```":
+        lines = lines[:-1]
+    return "\n".join(lines).strip()
+
+
+def parse_jsonish_value(text: str) -> Any:
+    candidate = _strip_code_fence(text)
+    if not candidate:
+        raise ValueError("empty payload")
+
+    try:
+        return json.loads(candidate)
+    except json.JSONDecodeError:
+        try:
+            return ast.literal_eval(candidate)
+        except (SyntaxError, ValueError) as exc:
+            raise ValueError("payload was not valid JSON or Python literal data") from exc
+
+
+def parse_runtime_payload(text: str) -> dict[str, Any]:
+    payload = parse_jsonish_value(text)
+    if not isinstance(payload, dict):
+        raise ValueError("runtime payload was not an object")
+    return payload
+
+
+def _titleize(key: str) -> str:
+    text = key.replace("_", " ").replace("-", " ").strip()
+    return " ".join(part.capitalize() for part in text.split()) or key
+
+
+def _truncate(text: str, limit: int = 160) -> str:
+    if len(text) <= limit:
+        return text
+    return f"{text[: limit - 1]}…"
+
+
+def _compact_text(value: Any, limit: int = 160) -> str:
+    if value is None:
+        return "None"
+    if isinstance(value, str):
+        collapsed = " ".join(value.split())
+        return _truncate(collapsed, limit=limit)
+    if isinstance(value, (int, float, bool)):
+        return str(value)
+    if isinstance(value, list):
+        if value and all(isinstance(item, (str, int, float, bool)) or item is None for item in value):
+            collapsed = ", ".join(_compact_text(item, limit=40) for item in value[:6])
+            if len(value) > 6:
+                collapsed = f"{collapsed}, …"
+            return _truncate(collapsed, limit=limit)
+    return _truncate(json.dumps(value, ensure_ascii=False, sort_keys=True), limit=limit)
+
+
+def _is_scalar(value: Any) -> bool:
+    if value is None or isinstance(value, (str, int, float, bool)):
+        return True
+    if isinstance(value, list):
+        return all(item is None or isinstance(item, (str, int, float, bool)) for item in value)
+    return False
+
+
+def _normalize_cell(value: Any) -> Any:
+    if value is None or isinstance(value, (str, int, float, bool)):
+        return value
+    return _compact_text(value)
+
+
+def _normalize_row(row: dict[str, Any]) -> dict[str, Any]:
+    return {str(key): _normalize_cell(value) for key, value in row.items()}
+
+
+def _column_rank(key: str) -> tuple[int, str]:
+    try:
+        return (_PREFERRED_COLUMN_ORDER.index(key), key)
+    except ValueError:
+        return (len(_PREFERRED_COLUMN_ORDER), key)
+
+
+def _metric_rank(key: str) -> tuple[int, str]:
+    try:
+        return (_PREFERRED_METRIC_KEYS.index(key), key)
+    except ValueError:
+        return (len(_PREFERRED_METRIC_KEYS), key)
+
+
+def _build_row_click(rows: list[dict[str, Any]]) -> dict[str, Any] | None:
+    for key in _URL_KEYS:
+        if any(isinstance(row.get(key), str) and row.get(key) for row in rows):
+            return {
+                "action": "openLink",
+                "url": f"{{{{ $event.{key} }}}}",
+            }
+    return None
+
+
+def _build_table_card(
+    title: str,
+    rows: list[dict[str, Any]],
+    *,
+    description: str | None = None,
+    empty_message: str = "No rows returned.",
+) -> dict[str, Any]:
+    if not rows:
+        return {
+            "type": "Card",
+            "children": [
+                {
+                    "type": "CardHeader",
+                    "children": [
+                        {"type": "CardTitle", "content": title},
+                        *(
+                            [{"type": "CardDescription", "content": description}]
+                            if description
+                            else []
+                        ),
+                    ],
+                },
+                {
+                    "type": "CardContent",
+                    "children": [
+                        {
+                            "type": "Alert",
+                            "variant": "default",
+                            "children": [
+                                {"type": "AlertTitle", "content": title},
+                                {"type": "AlertDescription", "content": empty_message},
+                            ],
+                        }
+                    ],
+                },
+            ],
+        }
+
+    normalized_rows = [_normalize_row(row) for row in rows]
+    all_keys = {key for row in normalized_rows for key in row}
+    visible_keys = sorted(all_keys, key=_column_rank)[:8]
+    columns: list[dict[str, Any]] = []
+    for key in visible_keys:
+        column: dict[str, Any] = {
+            "key": key,
+            "header": _titleize(key),
+            "sortable": key not in {"description"},
+        }
+        if any(isinstance(row.get(key), (int, float)) for row in normalized_rows):
+            column["align"] = "right"
+            column["format"] = "number"
+        if key in {"description"}:
+            column["maxWidth"] = "28rem"
+            column["sortable"] = False
+        columns.append(column)
+
+    data_table: dict[str, Any] = {
+        "type": "DataTable",
+        "columns": columns,
+        "rows": normalized_rows,
+        "search": len(normalized_rows) > 8,
+        "paginated": len(normalized_rows) > 10,
+        "pageSize": 10,
+    }
+
+    row_click = _build_row_click(rows)
+    if row_click is not None:
+        data_table["onRowClick"] = row_click
+
+    return {
+        "type": "Card",
+        "children": [
+            {
+                "type": "CardHeader",
+                "children": [
+                    {"type": "CardTitle", "content": title},
+                    *(
+                        [{"type": "CardDescription", "content": description}]
+                        if description
+                        else []
+                    ),
+                ],
+            },
+            {
+                "type": "CardContent",
+                "children": [data_table],
+            },
+        ],
+    }
+
+
+def _build_key_value_card(
+    title: str,
+    values: dict[str, Any],
+    *,
+    description: str | None = None,
+) -> dict[str, Any]:
+    rows = [{"field": _titleize(key), "value": _normalize_cell(value)} for key, value in values.items()]
+    return _build_table_card(
+        title,
+        rows,
+        description=description,
+        empty_message="No fields available.",
+    )
+
+
+def _select_metric_keys(values: dict[str, Any]) -> list[str]:
+    numeric_or_bool = [
+        key
+        for key, value in values.items()
+        if isinstance(value, (int, float, bool)) and not isinstance(value, str)
+    ]
+    prioritized = sorted(numeric_or_bool, key=_metric_rank)
+    return prioritized[:4]
+
+
+def _build_metric_grid(title: str, values: dict[str, Any]) -> dict[str, Any] | None:
+    metric_keys = _select_metric_keys(values)
+    if not metric_keys:
+        return None
+
+    tiles = [
+        {
+            "type": "Card",
+            "children": [
+                {
+                    "type": "CardContent",
+                    "cssClass": "p-6",
+                    "children": [
+                        {
+                            "type": "Metric",
+                            "label": _titleize(key),
+                            "value": values[key],
+                        }
+                    ],
+                }
+            ],
+        }
+        for key in metric_keys
+    ]
+
+    return {
+        "type": "Card",
+        "children": [
+            {
+                "type": "CardHeader",
+                "children": [{"type": "CardTitle", "content": title}],
+            },
+            {
+                "type": "CardContent",
+                "children": [
+                    {
+                        "type": "Grid",
+                        "gap": 4,
+                        "minColumnWidth": "12rem",
+                        "children": tiles,
+                    }
+                ],
+            },
+        ],
+    }
+
+
+def _looks_like_helper_envelope(value: Any) -> bool:
+    return isinstance(value, dict) and {"ok", "items", "item", "meta", "error"}.issubset(value.keys())
+
+
+def build_helper_sections(meta: dict[str, Any] | None) -> list[dict[str, Any]]:
+    if not isinstance(meta, dict) or not meta:
+        return []
+
+    sections: list[dict[str, Any]] = []
+    warning_bits: list[str] = []
+
+    if meta.get("error"):
+        warning_bits.append(str(meta["error"]))
+    if meta.get("truncated"):
+        truncated_by = str(meta.get("truncated_by") or "limit").replace("_", " ")
+        warning_bits.append(f"Helper output was truncated by {truncated_by}.")
+    if meta.get("lower_bound"):
+        warning_bits.append("Counts may be lower bounds rather than exact totals.")
+    if meta.get("more_available") is True:
+        warning_bits.append("More matching rows are available than were returned.")
+    if meta.get("more_available") == "unknown":
+        warning_bits.append("More matching rows may be available.")
+    if meta.get("next_request_hint"):
+        warning_bits.append(str(meta["next_request_hint"]))
+
+    if warning_bits:
+        sections.append(
+            {
+                "type": "Alert",
+                "variant": "warning",
+                "icon": "circle-alert",
+                "children": [
+                    {"type": "AlertTitle", "content": "Helper coverage notes"},
+                    {"type": "AlertDescription", "content": " ".join(warning_bits[:3])},
+                ],
+            }
+        )
+
+    badges: list[dict[str, Any]] = [{"type": "Small", "content": "Helper metadata"}]
+    for key in ("returned", "matched", "total", "scanned"):
+        value = meta.get(key)
+        if value is not None:
+            badges.append(
+                {
+                    "type": "Badge",
+                    "label": f"{key}: {value}",
+                    "variant": "outline",
+                }
+            )
+    source = meta.get("source")
+    if isinstance(source, str) and source:
+        badges.append({"type": "Muted", "content": source})
+
+    if len(badges) > 1:
+        sections.append(
+            {
+                "type": "Row",
+                "gap": 2,
+                "align": "center",
+                "cssClass": "flex-wrap",
+                "children": badges,
+            }
+        )
+
+    return sections
+
+
+def _render_list(
+    title: str,
+    value: list[Any],
+    *,
+    description: str | None = None,
+    depth: int = 0,
+) -> list[dict[str, Any]]:
+    if not value:
+        return [
+            {
+                "type": "Alert",
+                "variant": "default",
+                "children": [
+                    {"type": "AlertTitle", "content": title},
+                    {"type": "AlertDescription", "content": "No results returned."},
+                ],
+            }
+        ]
+
+    if all(isinstance(item, dict) for item in value):
+        rows = [item for item in value if isinstance(item, dict)]
+        return [_build_table_card(title, rows, description=description)]
+
+    rows = [
+        {
+            "index": index + 1,
+            "value": _normalize_cell(item),
+        }
+        for index, item in enumerate(value)
+    ]
+    return [_build_table_card(title, rows, description=description)]
+
+
+def _render_dict(
+    title: str,
+    value: dict[str, Any],
+    *,
+    description: str | None = None,
+    depth: int = 0,
+) -> list[dict[str, Any]]:
+    if depth > 2:
+        return [_build_key_value_card(title, value, description=description)]
+
+    if "results" in value or "coverage" in value:
+        sections: list[dict[str, Any]] = []
+        results = value.get("results")
+        coverage = value.get("coverage")
+        if results is not None:
+            sections.extend(_render_value("Results", results, depth=depth + 1))
+        if isinstance(coverage, dict):
+            sections.append(_build_key_value_card("Coverage", coverage))
+        remaining = {
+            key: item for key, item in value.items() if key not in {"results", "coverage"}
+        }
+        if remaining:
+            sections.extend(_render_dict(title, remaining, description=description, depth=depth + 1))
+        return sections
+
+    scalar_items = {key: item for key, item in value.items() if _is_scalar(item)}
+    nested_items = {key: item for key, item in value.items() if key not in scalar_items}
+
+    sections: list[dict[str, Any]] = []
+    metric_grid = _build_metric_grid(title, scalar_items)
+    metric_keys = set(_select_metric_keys(scalar_items))
+    if metric_grid is not None:
+        sections.append(metric_grid)
+
+    remaining_scalars = {
+        key: item for key, item in scalar_items.items() if key not in metric_keys
+    }
+    if remaining_scalars:
+        sections.append(_build_key_value_card(title, remaining_scalars, description=description))
+
+    for key, item in nested_items.items():
+        sections.extend(_render_value(_titleize(key), item, depth=depth + 1))
+
+    if not sections:
+        sections.append(_build_key_value_card(title, value, description=description))
+
+    return sections
+
+
+def _render_scalar(
+    title: str,
+    value: Any,
+    *,
+    description: str | None = None,
+) -> list[dict[str, Any]]:
+    return [
+        {
+            "type": "Card",
+            "children": [
+                {
+                    "type": "CardHeader",
+                    "children": [
+                        {"type": "CardTitle", "content": title},
+                        *(
+                            [{"type": "CardDescription", "content": description}]
+                            if description
+                            else []
+                        ),
+                    ],
+                },
+                {
+                    "type": "CardContent",
+                    "children": [{"type": "Text", "content": _compact_text(value, limit=500)}],
+                },
+            ],
+        }
+    ]
+
+
+def _render_value(
+    title: str,
+    value: Any,
+    *,
+    description: str | None = None,
+    depth: int = 0,
+) -> list[dict[str, Any]]:
+    if value is None:
+        return _render_scalar(title, "No result returned.", description=description)
+    if isinstance(value, dict):
+        return _render_dict(title, value, description=description, depth=depth)
+    if isinstance(value, list):
+        return _render_list(title, value, description=description, depth=depth)
+    return _render_scalar(title, value, description=description)
+
+
+def _result_count(value: Any) -> int | None:
+    if isinstance(value, list):
+        return len(value)
+    if _looks_like_helper_envelope(value):
+        items = value.get("items")
+        if isinstance(items, list):
+            return len(items)
+    if isinstance(value, dict) and isinstance(value.get("results"), list):
+        return len(value["results"])
+    return None
+
+
+def _build_summary_card(
+    query: str,
+    *,
+    runtime_meta: dict[str, Any] | None,
+    helper_meta: dict[str, Any] | None,
+    result: Any,
+) -> dict[str, Any]:
+    badges: list[dict[str, Any]] = []
+    row_count = _result_count(result)
+    if row_count is not None:
+        badges.append({"type": "Badge", "label": f"{row_count} rows", "variant": "outline"})
+    if isinstance(helper_meta, dict):
+        for key in ("total", "matched", "returned"):
+            if helper_meta.get(key) is not None:
+                badges.append(
+                    {
+                        "type": "Badge",
+                        "label": f"{key}: {helper_meta[key]}",
+                        "variant": "outline",
+                    }
+                )
+
+    summary_children: list[dict[str, Any]] = [
+        {
+            "type": "CardHeader",
+            "children": [
+                {"type": "CardTitle", "content": "Hub search results"},
+                {"type": "CardDescription", "content": query},
+            ],
+        }
+    ]
+
+    if badges:
+        summary_children.append(
+            {
+                "type": "CardContent",
+                "children": [
+                    {
+                        "type": "Row",
+                        "gap": 2,
+                        "align": "center",
+                        "cssClass": "flex-wrap",
+                        "children": badges,
+                    }
+                ],
+            }
+        )
+
+    return {"type": "Card", "children": summary_children}
+
+
+def build_runtime_wire(query: str, payload: dict[str, Any]) -> dict[str, Any]:
+    runtime_meta = payload.get("meta") if isinstance(payload.get("meta"), dict) else None
+    if isinstance(runtime_meta, dict) and runtime_meta.get("ok") is False:
+        return wrap_prefab_body(
+            extract_prefab_body_parts(error_wire(str(runtime_meta.get("error") or "Hub query failed")))[0],
+            meta=runtime_meta,
+        )
+
+    result = payload.get("result")
+    helper_meta: dict[str, Any] | None = None
+    body_children: list[dict[str, Any]] = []
+
+    if _looks_like_helper_envelope(result):
+        helper_meta = result.get("meta") if isinstance(result.get("meta"), dict) else None
+        if result.get("ok") is False:
+            message = str(result.get("error") or "Helper query failed")
+            body_children.append(
+                {
+                    "type": "Alert",
+                    "variant": "destructive",
+                    "icon": "circle-x",
+                    "children": [
+                        {"type": "AlertTitle", "content": "Hub helper error"},
+                        {"type": "AlertDescription", "content": message},
+                    ],
+                }
+            )
+        else:
+            helper_result: Any
+            items = result.get("items")
+            item = result.get("item")
+            if isinstance(items, list):
+                helper_result = items
+            elif item is not None:
+                helper_result = item
+            else:
+                helper_result = result
+            body_children.extend(_render_value("Results", helper_result))
+    else:
+        body_children.extend(_render_value("Results", result))
+
+    body_view = {
+        "type": "Column",
+        "gap": 6,
+        "cssClass": PAGE_CSS_CLASS,
+        "children": [
+            _build_summary_card(
+                query,
+                runtime_meta=runtime_meta,
+                helper_meta=helper_meta,
+                result=result,
+            ),
+            *build_helper_sections(helper_meta),
+            *body_children,
+        ],
+    }
+    return wrap_prefab_body(body_view, meta=runtime_meta)
+
+
+def parse_direct_wire(text: str) -> dict[str, Any]:
+    return coerce_wire(parse_jsonish_value(text))
+
+
+def parse_passthrough_wire(text: str) -> dict[str, Any]:
+    payload = parse_runtime_payload(text)
+    runtime_meta = payload.get("meta")
+    raw_wire = payload.get("result")
+
+    if raw_wire is None and isinstance(runtime_meta, dict):
+        error = runtime_meta.get("error")
+        if error:
+            body_view, state, defs, theme = extract_prefab_body_parts(error_wire(str(error)))
+            return wrap_prefab_body(
+                body_view,
+                meta=runtime_meta,
+                state=state,
+                defs=defs,
+                theme=theme,
+            )
+
+    if isinstance(raw_wire, (dict, list)):
+        try:
+            body_view, state, defs, theme = extract_prefab_body_parts(raw_wire)
+        except ValueError:
+            return build_runtime_wire("Hub query", payload)
+        return wrap_prefab_body(
+            body_view,
+            meta=runtime_meta if isinstance(runtime_meta, dict) else None,
+            state=state,
+            defs=defs,
+            theme=theme,
+        )
+
+    return build_runtime_wire("Hub query", payload)