UniProt MCP Server

A Model Context Protocol (MCP) server that provides access to UniProt protein information. This server allows AI assistants to fetch protein function and sequence information directly from UniProt.

Features

• Get protein information by UniProt accession number
• Batch retrieval of multiple proteins
• Caching for improved performance (24-hour TTL)
• Error handling and logging
• Information includes:
  • Protein name
  • Function description
  • Full sequence
  • Sequence length
  • Organism

Quick Start

1. Ensure you have Python 3.10 or higher installed

2. Clone this repository:

git clone https://github.com/TakumiY235/uniprot-mcp-server.git
   cd uniprot-mcp-server

3. Install dependencies:

# Using uv (recommended)
   uv pip install -r requirements.txt
   
   # Or using pip
   pip install -r requirements.txt

Configuration

Add to your Claude Desktop config file:

• Windows: `%APPDATA%\Claude\claude_desktop_config.json`
• macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
• Linux: `~/.config/Claude/claude_desktop_config.json`

{
  "mcpServers": {
    "uniprot": {
      "command": "uv",
      "args": ["--directory", "path/to/uniprot-mcp-server", "run", "uniprot-mcp-server"]
    }
  }
}

Usage Examples

After configuring the server in Claude Desktop, you can ask questions like:

Can you get the protein information for UniProt accession number P98160?

For batch queries:

Can you get and compare the protein information for both P04637 and P02747?

API Reference

Tools

1. `get_protein_info`

{
       "accession": "P12345",
       "protein_name": "Example protein",
       "function": ["Description of protein function"],
       "sequence": "MLTVX...",
       "length": 123,
       "organism": "Homo sapiens"
     }

2. `get_batch_protein_info`

Development

Setting up development environment

1. Clone the repository

2. Create a virtual environment:

python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install development dependencies:

pip install -e ".[dev]"

Running tests

pytest

Code style

This project uses:

• Black for code formatting
• isort for import sorting
• flake8 for linting
• mypy for type checking
• bandit for security checks
• safety for dependency vulnerability checks

Run all checks:

black .
isort .
flake8 .
mypy .
bandit -r src/
safety check

Technical Details

• Built using the MCP Python SDK
• Uses httpx for async HTTP requests
• Implements caching with 24-hour TTL using an OrderedDict-based cache
• Handles rate limiting and retries
• Provides detailed error messages

Error Handling

The server handles various error scenarios:

• Invalid accession numbers (404 responses)
• API connection issues (network errors)
• Rate limiting (429 responses)
• Malformed responses (JSON parsing errors)
• Cache management (TTL and size limits)

Contributing

We welcome contributions! Please feel free to submit a Pull Request. Here's how you can contribute:

1. Fork the repository

2. Create your feature branch (`git checkout -b feature/amazing-feature`)

3. Commit your changes (`git commit -m 'Add some amazing feature'`)

4. Push to the branch (`git push origin feature/amazing-feature`)

5. Open a Pull Request

Please make sure to update tests as appropriate and adhere to the existing coding style.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• UniProt for providing the protein data API
• Anthropic for the Model Context Protocol specification
• Contributors who help improve this project