Hugging Face Hub Semantic Search MCP Server
⚠️ Note: This is an unofficial MCP server inspired by Hugging Face's official MCP server. It may be deprecated at any time if official functionality supersedes it. For the official server, see hf.co/mcp.
An MCP (Model Context Protocol) server that provides semantic search capabilities for Hugging Face models and datasets. This server enables Claude and other MCP-compatible clients to search, discover, and explore the Hugging Face ecosystem using natural language queries.
Features
- Semantic Search: AI-powered similarity search (not just keyword matching)
- Dataset Search: Find datasets based on natural language descriptions
- Model Search: Find models with optional parameter count filtering
- Similarity Search: Find similar models/datasets to a given one
- Trending Content: Get currently trending models and datasets
- Detailed Metadata: Access comprehensive technical information via HuggingFace API
- Model/Dataset Cards: Download README cards for detailed information
Tools Available
Dataset Tools
search_datasets: Search datasets using natural language queriesfind_similar_datasets: Find datasets similar to a specified oneget_trending_datasets: Get currently trending datasetsget_dataset_info: Get detailed metadata for a specific datasetdownload_dataset_card: Download README card for a dataset
Model Tools
search_models: Search models using natural language queries with parameter filteringfind_similar_models: Find models similar to a specified oneget_trending_models: Get currently trending models with parameter filteringget_model_info: Get detailed metadata for a specific modelget_model_safetensors_metadata: Get model architecture details and parameter count from safetensorsdownload_model_card: Download README card for a model
Installation
Prerequisites
- UV - Fast Python package installer
- Claude Desktop or another MCP-compatible client
Quick Start
No installation needed! UV will automatically fetch and run the server.
Configuration
Claude Desktop Setup
Add the following to your Claude Desktop configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"huggingface-hub-search": {
"command": "uvx",
"args": [
"git+https://github.com/davanstrien/hub-semantic-search-mcp.git"
],
"env": {
"HF_SEARCH_API_URL": "https://davanstrien-huggingface-datasets-search-v2.hf.space"
}
}
}
}
Alternative: Local Development Setup
If you want to contribute or modify the code:
# Clone the repository
git clone https://github.com/davanstrien/hub-semantic-search-mcp.git
cd hub-semantic-search-mcp
# Install dependencies with UV
uv sync
Then configure Claude Desktop to use the local version:
{
"mcpServers": {
"huggingface-hub-search": {
"command": "uv",
"args": [
"--directory",
"/path/to/hub-semantic-search-mcp",
"run",
"python",
"app.py"
],
"env": {
"HF_SEARCH_API_URL": "https://davanstrien-huggingface-datasets-search-v2.hf.space"
}
}
}
}
Usage Examples
Once configured, you can use the tools in Claude Desktop:
Search for Datasets
"Find datasets about climate change and weather patterns"
Search for Models
"Find small language models under 1B parameters for text generation"
Find Similar Content
"Find datasets similar to 'squad' for question answering"
Get Trending Content
"Show me the top 10 trending AI models this week"
Get Detailed Metadata
"Get detailed information about the 'stanford-nlp/imdb' dataset" "Show me technical details and configuration for 'microsoft/DialoGPT-medium'" "What's the parameter count and architecture of 'microsoft/DialoGPT-medium'?"
Download Documentation
"Download the model card for 'microsoft/DialoGPT-medium'"
Environment Variables
HF_SEARCH_API_URL: Base URL for the search API (default: https://davanstrien-huggingface-datasets-search-v2.hf.space)
Search Backend
This MCP server connects to a semantic search API that indexes Hugging Face models and datasets with AI-generated summaries. The search uses embedding-based similarity rather than keyword matching, making it more effective for discovering relevant content based on intent and meaning.
Development
Running Locally
# Run the server directly
uv run python app.py
# Or activate the virtual environment
uv shell
python app.py
Testing with MCP Inspector
# Test the GitHub version
npx @modelcontextprotocol/inspector uvx git+https://github.com/davanstrien/hub-semantic-search-mcp.git
# Or test locally
npx @modelcontextprotocol/inspector uv run python app.py
Contributing
Contributions are welcome! Please feel free to submit issues and pull requests.
Development Setup
git clone https://github.com/davanstrien/hub-semantic-search-mcp.git
cd hub-semantic-search-mcp
uv sync --dev
License
MIT License - see LICENSE file for details.
Related Projects
- Model Context Protocol
- Hugging Face Hub
- Claude Desktop
- UV - Fast Python package installer