Hugging Face MCP Server

# 🤗 Hugging Face MCP Server 🤗 [](https://smithery.ai/server/@shreyaskarnik/huggingface-mcp-server) A Model Context Protocol (MCP) server that provides read-only access to the Hugging Face Hub APIs. This server allows LLMs like Claude to interact with Hugging Face's models, datasets, spaces, papers, and collections. ## Components ### Resources The server exposes popular Hugging Face resources: - Custom `hf://` URI scheme for accessing resources - Models with `hf://model/{model_id}` URIs - Datasets with `hf://dataset/{dataset_id}` URIs - Spaces with `hf://space/{space_id}` URIs - All resources have descriptive names and JSON content type ### Prompts The server provides two prompt templates: - `compare-models`: Generates a comparison between multiple Hugging Face models - Required `model_ids` argument (comma-separated model IDs) - Retrieves model details and formats them for comparison - `summarize-paper`: Summarizes a research paper from Hugging Face - Required `arxiv_id` argument for paper identification - Optional `detail_level` argument (brief/detailed) to control summary depth - Combines paper metadata with implementation details ### Tools The server implements several tool categories: - **Model Tools** - `search-models`: Search models with filters for query, author, tags, and limit - `get-model-info`: Get detailed information about a specific model - **Dataset Tools** - `search-datasets`: Search datasets with filters - `get-dataset-info`: Get detailed information about a specific dataset - **Space Tools** - `search-spaces`: Search Spaces with filters including SDK type - `get-space-info`: Get detailed information about a specific Space - **Paper Tools** - `get-paper-info`: Get information about a paper and its implementations - `get-daily-papers`: Get the list of curated daily papers - **Collection Tools** - `search-collections`: Search collections with various filters - `get-collection-info`: Get detailed information about a specific collection ## Configuration The server does not require configuration, but supports optional Hugging Face authentication: - Set `HF_TOKEN` environment variable with your Hugging Face API token for: - Higher API rate limits - Access to private repositories (if authorized) - Improved reliability for high-volume requests ## Quickstart ### Install #### Installing via Smithery To install huggingface-mcp-server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@shreyaskarnik/huggingface-mcp-server): ```bash npx -y @smithery/cli install @shreyaskarnik/huggingface-mcp-server --client claude ``` #### Claude Desktop On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json` On Windows: `%APPDATA%/Claude/claude_desktop_config.json` <details> <summary>Development/Unpublished Servers Configuration</summary> ```json "mcpServers": { "huggingface": { "command": "uv", "args": [ "--directory", "/absolute/path/to/huggingface-mcp-server", "run", "huggingface_mcp_server.py" ], "env": { "HF_TOKEN": "your_token_here" // Optional } } } ``` </details> ## Development ### Building and Publishing To prepare the package for distribution: 1. Sync dependencies and update lockfile: ```bash uv sync ``` 1. Build package distributions: ```bash uv build ``` This will create source and wheel distributions in the `dist/` directory. 1. Publish to PyPI: ```bash uv publish ``` Note: You'll need to set PyPI credentials via environment variables or command flags: - Token: `--token` or `UV_PUBLISH_TOKEN` - Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD` ### Debugging Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector). You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command: ```bash npx @modelcontextprotocol/inspector uv --directory /path/to/huggingface-mcp-server run huggingface_mcp_server.py ``` Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging. ## Example Prompts for Claude When using this server with Claude, try these example prompts: - "Search for BERT models on Hugging Face with less than 100 million parameters" - "Find the most popular datasets for text classification on Hugging Face" - "What are today's featured AI research papers on Hugging Face?" - "Summarize the paper with arXiv ID 2307.09288 using the Hugging Face MCP server" - "Compare the Llama-3-8B and Mistral-7B models from Hugging Face" - "Show me the most popular Gradio spaces for image generation" - "Find collections created by TheBloke that include Mixtral models" ## Troubleshooting If you encounter issues with the server: 1. Check server logs in Claude Desktop: - macOS: `~/Library/Logs/Claude/mcp-server-huggingface.log` - Windows: `%APPDATA%\Claude\logs\mcp-server-huggingface.log` 2. For API rate limiting errors, consider adding a Hugging Face API token 3. Make sure your machine has internet connectivity to reach the Hugging Face API 4. If a particular tool is failing, try accessing the same data through the Hugging Face website to verify it exists