iflow-mcp_hub-semantic-search-mcp 0.1.0
MCP server for semantic search of Hugging Face models and datasets
Requires Python: >=3.8
Keywords: ai, claude, huggingface, mcp, model-context-protocol, semantic-search

# 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