Inspect the current Hugging Face authentication context, including the account, visible organization memberships, and credential access details. Read-only and never returns credential values.
ConnectorNo auth
730,744 tools. Updated 2026-10-31 22:26
"Hugging Face" matching MCP tools:
- Search Hugging Face repositories with a shared query interface. You can target models, datasets, spaces, or aggregate across multiple repo types in one call. Include links to repositories in your response.ConnectorNo auth
- Get details for one or more Hugging Face repos (model, dataset, or space). Auto-detects type unless specified. For datasets, use operations: overview, dataset_structure, dataset_preview. Use dataset_structure first to discover configs, splits, sizes, and schema. Use dataset_preview only when config and split are known, unless the dataset has a single config/split.ConnectorNo auth
- Hugging Face trending models & Spaces (source: Hugging Face Hub API). Full current feed, refreshed daily. Requires API key.ConnectorNo auth
- ✅ No API key needed — call this now. Listing: hfspaces: New Hugging Face Spaces — AI app demos & live web UIs. Price 0.01 USDC/query (max 20 queries/session). Sample questions: What AI app demos were just created on Hugging Face Spaces?; Show the newest gradio or streamlit Spaces shipped this hour. FREE preview — no key, no payment. Try one of the sample questions now.ConnectorNo auth
- Get recent AI/ML research papers from one of three feeds, chosen with the source argument. arxiv_recent is the firehose: newest arXiv submissions in cs.AI / cs.LG / cs.CL / cs.CV by submission date, refreshed daily at 11:30 UTC. trending is citation-ranked from Semantic Scholar across five fan-out queries, deduped, refreshed daily at 11:00 UTC. hf_daily is Hugging Face editor-curated with community upvotes and discussion counts, refreshed daily at 14:15 UTC. Pick arxiv_recent for what is brand new, trending for what is influential, hf_daily for what practitioners are discussing. License: arXiv and Semantic Scholar permit metadata use; the standard attribution block ships on every response.ConnectorNo auth
Matching MCP Servers
- AlicenseBqualityDmaintenanceA Model Context Protocol server that provides Claude and other LLMs with read-only access to Hugging Face Hub APIs, enabling interaction with models, datasets, spaces, papers, and collections through natural language.1072MIT
- AlicenseBqualityCmaintenanceEnables LLMs to search and retrieve read-only information from the Hugging Face Hub about models, datasets, Spaces, papers, and collections. It also supports model comparison and paper summarization prompts.10MIT
Matching MCP Connectors
Connect to Hugging Face Hub and thousands of Gradio AI Applications
Find GitHub tools and Hugging Face models: source links, licenses, setup notes and review dates.
- USE ONLY for questions about published THRESHOLDS, tolerance, rules or SOURCES without grading the user's supplied numeric ratios. For PSA 10 compatibility with supplied ratios use grade_centering instead; it applies rounding and house-specific rules. If face/axis is unknown, ask first without any call. Example: {"house":"BGS","face":"back"}; fields optional, {} returns all. Houses PSA/BGS/CGC/SGC (case accepted), TAG excluded: explain proprietary assessment; never substitute another house. Face front/back in English; case/spaces accepted, reverse/obverse and translations rejected. SGC back has empty bands, no published table or ceiling; never invent one. Returns official/interpreted/approximate status, sources and verification. Not a final-grade prediction.ConnectorNo auth
- Finds every FACE in a video clip and stores one independent path per person — position, size and head rotation (nod/turn/tilt) per frame. Queued, not immediate: poll list_face_tracks until state is 'ready'. Re-running on the same clip REPLACES its previous run and keeps the same trackId, so clips already bound stay bound. A person who leaves the shot and returns much later becomes a SECOND face on purpose — use merge_faces if they are the same person.ConnectorDestructiveNo auth
- utilpeek: Scan text for leaked secrets: API keys, tokens, private keys and seed phrases. For personal data use redact_pii; for injected instructions use check_text_for_prompt_injection. Returns every API key, token, private key or BIP-39 seed phrase in a text (AWS, GitHub, GitLab, Stripe, Slack, Google, OpenAI and other sk- keys, Hugging Face, npm, PyPI, SendGrid, Telegram, PEM and hex keys, JWTs, URL passwords), each with type, line, column, offset, confidence and a redacted preview. The secret itself is never returned: only its type, position and a redacted preview (a vendor prefix and at most 4 characters at each end; a seed phrase only its word count). Pattern-based: a clean result is no proof. Blank input is a 422 (not charged). Typically under 0.5 s. Price: USD 0.002. Free: 10 utilpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/secret-scan-api.htmlConnectorNo auth
- utilpeek: Scan text for leaked secrets: API keys, tokens, private keys and seed phrases. For personal data use redact_pii; for injected instructions use check_text_for_prompt_injection. Returns every API key, token, private key or BIP-39 seed phrase in a text (AWS, GitHub, GitLab, Stripe, Slack, Google, OpenAI and other sk- keys, Hugging Face, npm, PyPI, SendGrid, Telegram, PEM and hex keys, JWTs, URL passwords), each with type, line, column, offset, confidence and a redacted preview. The secret itself is never returned: only its type, position and a redacted preview (a vendor prefix and at most 4 characters at each end; a seed phrase only its word count). Pattern-based: a clean result is no proof. Blank input is a 422 (not charged). Typically under 0.5 s. Price: USD 0.002. Free: 10 utilpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/secret-scan-api.htmlConnectorNo auth
- utilpeek: Scan text for leaked secrets: API keys, tokens, private keys and seed phrases. For personal data use redact_pii; for injected instructions use check_text_for_prompt_injection. Returns every API key, token, private key or BIP-39 seed phrase in a text (AWS, GitHub, GitLab, Stripe, Slack, Google, OpenAI and other sk- keys, Hugging Face, npm, PyPI, SendGrid, Telegram, PEM and hex keys, JWTs, URL passwords), each with type, line, column, offset, confidence and a redacted preview. The secret itself is never returned: only its type, position and a redacted preview (a vendor prefix and at most 4 characters at each end; a seed phrase only its word count). Pattern-based: a clean result is no proof. Blank input is a 422 (not charged). Typically under 0.5 s. Price: USD 0.002. Free: 10 utilpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/secret-scan-api.htmlConnectorNo auth
- utilpeek: Scan text for leaked secrets: API keys, tokens, private keys and seed phrases. For personal data use redact_pii; for injected instructions use check_text_for_prompt_injection. Returns every API key, token, private key or BIP-39 seed phrase in a text (AWS, GitHub, GitLab, Stripe, Slack, Google, OpenAI and other sk- keys, Hugging Face, npm, PyPI, SendGrid, Telegram, PEM and hex keys, JWTs, URL passwords), each with type, line, column, offset, confidence and a redacted preview. The secret itself is never returned: only its type, position and a redacted preview (a vendor prefix and at most 4 characters at each end; a seed phrase only its word count). Pattern-based: a clean result is no proof. Blank input is a 422 (not charged). Typically under 0.5 s. Price: USD 0.002. Free: 10 utilpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/secret-scan-api.htmlConnectorNo auth
- FREE analyzability check before paying for portrait_analysis: is a face detectable, is the photo quality good enough (clipping/color cast)? Deterministic, no LLM. 10/hour/IP.ConnectorNo auth
- FREE analyzability check before paying for portrait_analysis: is a face detectable, is the photo quality good enough (clipping/color cast)? Deterministic, no LLM. 10/hour/IP.ConnectorNo auth
- FREE analyzability check before paying for portrait_analysis: is a face detectable, is the photo quality good enough (clipping/color cast)? Deterministic, no LLM. 10/hour/IP.ConnectorNo auth
- Currency-split income (or expense) totals in ONE compact call: face totals grouped by each transaction currency (e.g. EUR face vs GBP face) PLUS the company functional-currency total for the same set. Use when the user asks "total income in EUR and in GBP", "how much came in in euros vs pounds", "income by currency", or wants face vs converted figures before an FX check. Do NOT page get_transactions to sum — this returns aggregates only (no transaction dump). Defaults to money-in (income); pass direction "out" for spend. Covers bank lines only: invoices and ledger revenue are NOT included, and a company with no bank account returns nothing here, which is not "no income". For income as the books record it, use get_profit_and_loss.ConnectorNo auth
- Currency-split income (or expense) totals in ONE compact call: face totals grouped by each transaction currency (e.g. EUR face vs GBP face) PLUS the company functional-currency total for the same set. Use when the user asks "total income in EUR and in GBP", "how much came in in euros vs pounds", "income by currency", or wants face vs converted figures before an FX check. Do NOT page get_transactions to sum — this returns aggregates only (no transaction dump). Defaults to money-in (income); pass direction "out" for spend. Covers bank lines only: invoices and ledger revenue are NOT included, and a company with no bank account returns nothing here, which is not "no income". For income as the books record it, use get_profit_and_loss.ConnectorNo auth
- Have ORIVOX's own AI build the site from a chat message -- the caller supplies no HTML. One call = one user message; Orivox owns the multi-turn conversation per project. For clients with no model of their own (the Hugging Face Space front end). If YOU can author HTML yourself, prefer create_project + create_new_site: it is faster and does not spend the user's Orivox AI credits -- every build_site turn runs on Orivox's own AI and is metered against the user's account. Call create_project first, then relay each user message here verbatim. Response phases: - "collecting": Orivox needs more detail -- show `reply` (its clarifying question) to the user and call again with their answer. - "built": the site is live -- share `preview_url` (and `answer`, its own summary of what it built).ConnectorOAuth
- One model's observation: latest verdict, negative snapshots, anchored epochs, silence proof pointer, archive silence, badge and record URL. Required model is a Hugging Face id, org/name, for example Qwen/Qwen3-32B. An empty model is an error. An exact id is used; otherwise one case-insensitive substring match is accepted, and several matches return candidates instead of a verdict. This is not a catalog page, not the weekly report, and not the seal bytes. Reads published files only.ConnectorNo auth
- When to use: Hugging Face Hub models, datasets, Spaces, collections, papers, daily papers, today's trending models, current paper leaderboard, docs, and repository files. Examples: {"operations":[{"cmd":"ls","args":["hf://models/trending","--limit","10"]}]} {"operations":[{"cmd":"ls","args":["hf://papers/trending"]}]} {"operations":[{"cmd":"ls","args":["hf://papers/daily/latest"]}]} {"operations":[{"cmd":"cat","args":["hf://papers/2501.00001/paper.md"]}]} {"operations":[{"cmd":"search","args":["hf://models","--tag","base_model:finetune:org/repo"]}]} Use hf_fs for Hugging Face Hub filesystem operations. Call it with operations, an array of {cmd, args} items; multiple operations may be submitted together. Usage: {"operations":[{"cmd":"ls","args":["hf://models/org/repo"]}]} Grammar; each string below is one args array item: ls URI [--recursive] [--glob GLOB] [--type TYPE] [--sort SORT] [--limit N] cat URI [--offset N] [--max-bytes N] attach URI [--max-bytes N] stat URI find URI [--name GLOB] [--path GLOB] [--type TYPE] [--limit N] search URI [QUERY] [--type TYPE] [--sort SORT] [--tag TAG] [--kind mcp] [--limit N] COMMAND = ls|cat|attach|stat|find|search. TYPE = file|dir|repo|bucket|collection|paper|link. SORT = createdAt|downloads|likes|lastModified|likes30d|trendingScore|mainSize|id|trending|upvotes. URI is a canonical hf:// URI. QUERY and GLOB are each one string. Use search for resource discovery, not repository-content search; ls for a known directory, find for recursive file discovery by name/path (not file contents), stat for filesystem metadata or an uncertain target type, cat for text contents, and attach for a complete JPEG, PNG, or WebP image. When the request gives an exact text-file URI, use cat directly; do not add ls or stat first. stat does not read the contents of JSON, Markdown, or other text files. Search scopes: hf://models[/OWNER], hf://datasets[/OWNER], hf://spaces[/OWNER], hf://collections[/OWNER], hf://papers, and hf://docs[/...]. Repository and repository-file scopes are not supported: search a resource root or owner scope to discover resources; use find for file discovery within a repository or cat for a known text file. Paper and documentation search require QUERY. --tag filters models, datasets, and Spaces by Hub tag (repeatable; all must match) and applies only to hf://models|datasets|spaces[/OWNER]; omit QUERY when the tag already identifies the target, because QUERY also filters repository names. Derived models carry base_model:ID and base_model:RELATION:ID tags, where RELATION is finetune, adapter, merge, or quantized. --kind applies only to hf://spaces[/OWNER]; the only valid --kind value is mcp, which selects MCP Spaces. Tag families are documented at hf://README.md. Use ls hf://models/trending, hf://datasets/trending, hf://spaces/trending, or hf://papers/trending for trending listings. hf://papers/ID is a paper directory, not paper text. Use cat hf://papers/ID/paper.md for paper text and cat hf://papers/ID/metadata.json for metadata. No preliminary listing is needed for these known paths. Use ls hf://papers/ID to discover other resources. Omit --limit, --sort, and --type unless the request requires them. Limits and path-specific behavior are documented at hf://README.md. Issue one hf_fs call.ConnectorNo auth
- Run an AI transform on a hosted video via Replicate (async): op 'bg-remove' or 'face-swap' (face-swap needs imageUrl). Returns { id, status } — poll neuron_studio_transform_video_status for videoUrl. Needs the matching model version configured.ConnectorNo auth