Hugging Face Hub Semantic Search MCP

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 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 - 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"

"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.

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

An unofficial MCP server that provides semantic search capabilities for Hugging Face models and datasets, enabling Claude and other MCP-compatible clients to search, discover, and explore the Hugging Face ecosystem using natural language queries.

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