# Hugging Face MCP server

Connect to Hugging Face Hub and thousands of Gradio AI Applications

## Links
- Registry page: https://www.getdrio.com/mcp/co-huggingface-hf-mcp-server

## Install
- Endpoint: https://huggingface.co/mcp
- Auth: Auth required by registry metadata

## Setup notes
- Remote header: Authorization (secret)
- The upstream registry signals required auth or secrets.
- Remote endpoint: https://huggingface.co/mcp?login
- Remote endpoint: https://huggingface.co/mcp
- Header: Authorization

## Tools
- hf_whoami - Hugging Face tools are being used anonymously and may be rate limited. Call this tool for instructions on joining and authenticating. Endpoint: https://huggingface.co/mcp
- space_search - 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. Endpoint: https://huggingface.co/mcp
- hub_repo_search - 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. Endpoint: https://huggingface.co/mcp
- paper_search - 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. Endpoint: https://huggingface.co/mcp
- hub_repo_details - 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. Endpoint: https://huggingface.co/mcp
- hf_doc_search - 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 16 May 2026. Combine with the Product filter to focus results. Endpoint: https://huggingface.co/mcp
- hf_doc_fetch - Fetch a document from the Hugging Face or Gradio documentation library. For large documents, use offset to get subsequent chunks. Endpoint: https://huggingface.co/mcp
- gr1_z_image_turbo_generate - 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 the Z-Image diffusion transformer pipeline. Returns: tuple: (gallery_images, seed_str, seed_int), - seed_str: String representation of the seed used for generation, - seed_int: Integer representation of the seed used for generation (from mcp-tools/Z-Image-Turbo) Endpoint: https://huggingface.co/mcp

## Resources
Not captured

## Prompts
- User Summary - Generate a summary of a Hugging Face user including their profile, models, datasets, spaces, and papers. Enter either a username (e.g., "clem") or a Hugging Face profile URL (e.g., "hf.co/julien-c" or "huggingface.co/thomwolf"). Arguments: user_id
- Paper Summary - Generate a comprehensive summary of an arXiv paper including its details and related models, datasets, and spaces on Hugging Face. Accepts various formats: "2502.16161", "arxiv:2502.16161", "https://arxiv.org/abs/2502.16161", or Hugging Face paper URLs. Arguments: paper_id
- Model Details - Get detailed information about a model from the Hugging Face Hub. Includes README from the repository - review before use. Arguments: model_id
- Dataset Details - Get detailed information about a dataset from the Hugging Face Hub. Includes README from the repository - review before use. Arguments: dataset_id

## Metadata
- Owner: co.huggingface
- Version: 0.2.33
- Runtime: Streamable Http
- Transports: HTTP
- License: Not captured
- Language: Not captured
- Stars: Not captured
- Updated: Oct 22, 2025
- Source: https://registry.modelcontextprotocol.io