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

Ensure you have Python 3.10 or higher installed

Clone this repository:

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

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

get_protein_info

Get information for a single protein
Required parameter: accession (UniProt accession number)

Example response:

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

get_batch_protein_info
- Get information for multiple proteins
- Required parameter: accessions (array of UniProt accession numbers)
- Returns an array of protein information objects

Development

Setting up development environment

Clone the repository

Create a virtual environment:

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

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:

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
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