Summary
Connects Claude to the Hugging Face Hub API for browsing and inspecting models, datasets, and Spaces. You get search across all three categories, detailed metadata retrieval, file listings at the repo root, and trending feeds. Useful when you need Claude to recommend models for a task, pull dataset documentation, or check what files ship with a particular checkpoint. Part of the Pipeworx gateway, so you can either connect to this standalone endpoint or use the full gateway with its ask_pipeworx natural language wrapper that routes your questions to the right tool automatically.
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Tools
Public tool metadata for what this MCP can expose to an agent.
8 tools
hf_whoamiHugging Face tools are being used anonymously and may be rate limited. Call this tool for instructions on joining and authenticating.
Hugging Face tools are being used anonymously and may be rate limited. Call this tool for instructions on joining and authenticating.
No parameter schema in public metadata yet.
space_searchFind Hugging Face Spaces using semantic search. IMPORTANT Only MCP Servers can be used with the dynamic_space toolInclude links to the Space when presenting the results.3 params
Find Hugging Face Spaces using semantic search. IMPORTANT Only MCP Servers can be used with the dynamic_space toolInclude links to the Space when presenting the results.
Parameters* required
mcpbooleanOnly return MCP Server enabled Spacesdefault: false
limitnumberNumber of results to returndefault: 10
querystringSemantic Search Query
hub_repo_searchSearch Hugging Face repositories with a shared query interface. You can target models, datasets, spaces, or aggregate across multiple repo types in one call. Use space_search for semantic-first discovery of Spaces. Include links to repositories in your response.6 params
Search Hugging Face repositories with a shared query interface. You can target models, datasets, spaces, or aggregate across multiple repo types in one call. Use space_search for semantic-first discovery of Spaces. Include links to repositories in your response.
Parameters* required
sortstringSort order (descending): trendingScore, downloads, likes, createdAt, lastModifiedone of
trendingScore · downloads · likes · createdAt · lastModifiedlimitnumberMaximum number of results to return per selected repo typedefault: 20
querystringSearch term. Leave blank and specify sort + limit to browse trending or recent repositories.
authorstringOrganization or user namespace to filter by (e.g. 'google', 'meta-llama', 'huggingface').
filtersarrayOptional hub filter tags. Applied to each selected repo type (e.g. ["text-generation"], ["language:en"], ["mcp-server"]).
repo_typesarrayRepository types to search. Defaults to ["model", "dataset"]. space uses keyword search via /api/spaces.
paper_searchFind Machine Learning research papers on the Hugging Face hub. Include 'Link to paper' When presenting the results. Consider whether tabulating results matches user intent.3 params
Find Machine Learning research papers on the Hugging Face hub. Include 'Link to paper' When presenting the results. Consider whether tabulating results matches user intent.
Parameters* required
querystringSemantic Search query
concise_onlybooleanReturn a 2 sentence summary of the abstract. Use for broad search terms which may return a lot of results. Check with User if unsure.default: false
results_limitnumberNumber of results to returndefault: 12
hub_repo_detailsGet details for one or more Hugging Face repos (model, dataset, or space). Auto-detects type unless specified.2 params
Get details for one or more Hugging Face repos (model, dataset, or space). Auto-detects type unless specified.
Parameters* required
repo_idsarrayRepo IDs for (models|dataset/space) - usually in author/name format (e.g. openai/gpt-oss-120b)
repo_typestringSpecify lookup type; otherwise auto-detectsone of
model · dataset · spacehf_doc_searchSearch and Discover Hugging Face Product and Library documentation. Send an empty query to discover structure and navigation instructions. Knowledge up-to-date as at 11 March 2026. Combine with the Product filter to focus results.2 params
Search and Discover Hugging Face Product and Library documentation. Send an empty query to discover structure and navigation instructions. Knowledge up-to-date as at 11 March 2026. Combine with the Product filter to focus results.
Parameters* required
querystringStart with an empty query for structure, endpoint discovery and navigation tips. Use semantic queries for targetted searches.
productstringFilter by Product. Supply when known for focused results
hf_doc_fetchFetch a document from the Hugging Face or Gradio documentation library. For large documents, use offset to get subsequent chunks.2 params
Fetch a document from the Hugging Face or Gradio documentation library. For large documents, use offset to get subsequent chunks.
Parameters* required
offsetnumberToken offset for large documents (use the offset from truncation message)
doc_urlstringDocumentation URL (Hugging Face or Gradio)
gr1_z_image_turbo_generateGenerate an image using the Z-Image model based on the provided prompt and settings. This function is triggered when the user clicks the "Generate" button. It processes the input prompt (optionally enhancing it), configures generation parameters, and produces an image using th...6 params
Generate an image using the Z-Image model based on the provided prompt and settings. This function is triggered when the user clicks the "Generate" button. It processes the input prompt (optionally enhancing it), configures generation parameters, and produces an image using th...
Parameters* required
seedintegerSeed for reproducible generationdefault: 42
shiftnumberTime shift parameter for the flow matching schedulerdefault: 3
stepsnumberNumber of inference steps for the diffusion processdefault: 8
promptstringText prompt describing the desired image content
resolutionstringOutput resolution in format "WIDTHxHEIGHT ( RATIO )" (e.g., "1024x1024 ( 1:1 )")one of
1024x1024 ( 1:1 ) · 1152x896 ( 9:7 ) · 896x1152 ( 7:9 ) · 1152x864 ( 4:3 ) · 864x1152 ( 3:4 ) · 1248x832 ( 3:2 )default: 1024x1024 ( 1:1 )random_seedbooleanWhether to generate a new random seed, if True will ignore the seed inputdefault: true
mcp-huggingface
Hugging Face Hub MCP — models, datasets, spaces
Part of Pipeworx — an MCP gateway connecting AI agents to 673+ live data sources.
Tools
| Tool | Description |
|---|---|
search_models | Browse / search models on the Hub. |
search_datasets | Browse / search datasets on the Hub. |
search_spaces | Browse / search Spaces (demo apps). |
get_model | Detailed model info — config, tags, downloads, files at root. |
get_dataset | Detailed dataset info. |
get_space | Detailed Space info. |
list_model_files | List files at the root of a model repo. |
list_dataset_files | List files in a dataset repo. |
trending_models | Currently-trending models on the Hub. |
trending_datasets | Currently-trending datasets. |
Quick Start
Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):
{
"mcpServers": {
"huggingface": {
"url": "https://gateway.pipeworx.io/huggingface/mcp"
}
}
}
Or connect to the full Pipeworx gateway for access to all 673+ data sources:
{
"mcpServers": {
"pipeworx": {
"url": "https://gateway.pipeworx.io/mcp"
}
}
}
Using with ask_pipeworx
Instead of calling tools directly, you can ask questions in plain English:
ask_pipeworx({ question: "your question about Huggingface data" })
The gateway picks the right tool and fills the arguments automatically.
More
License
MIT
Featured
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
