https://github.com/davanstrien/hub-semantic-search-mcp
https://github.com/davanstrien/hub-semantic-search-mcp
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/davanstrien/hub-semantic-search-mcp
- Owner: davanstrien
- License: mit
- Created: 2025-06-09T10:08:30.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-09T10:39:06.000Z (4 months ago)
- Last Synced: 2025-06-28T17:59:34.227Z (3 months ago)
- Language: Python
- Size: 23.4 KB
- Stars: 16
- Watchers: 0
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](https://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 queries
- `find_similar_datasets`: Find datasets similar to a specified one
- `get_trending_datasets`: Get currently trending datasets
- `get_dataset_info`: Get detailed metadata for a specific dataset
- `download_dataset_card`: Download README card for a dataset
### Model Tools
- `search_models`: Search models using natural language queries with parameter filtering
- `find_similar_models`: Find models similar to a specified one
- `get_trending_models`: Get currently trending models with parameter filtering
- `get_model_info`: Get detailed metadata for a specific model
- `get_model_safetensors_metadata`: Get model architecture details and parameter count from safetensors
- `download_model_card`: Download README card for a model
## Installation
### Prerequisites
- [UV](https://docs.astral.sh/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`
```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:
```bash
# 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:
```json
{
"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
```bash
# Run the server directly
uv run python app.py
# Or activate the virtual environment
uv shell
python app.py
```
### Testing with MCP Inspector
```bash
# 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
```bash
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](https://github.com/modelcontextprotocol/python-sdk)
- [Hugging Face Hub](https://huggingface.co/docs/hub/)
- [Claude Desktop](https://claude.ai/download)
- [UV](https://docs.astral.sh/uv/) - Fast Python package installer