UniProt MCP Server
About
Access UniProt protein information, including function and sequence data.
Details
- Author
- TakumiY235
- GitHub stars
- 12
- Downloads
- 223
- Categories
- Database, Other
Jump to
- Fetch protein info by single UniProt accession
- Batch retrieval of multiple proteins
- Built‑in caching with a 24‑hour TTL
- Error handling for invalid accessions, network issues, and rate limits
- Returns protein name, function, full sequence, length, and organism
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
UniProt MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install Python 3.10 or higher, clone the repository, and install dependencies with uv pip install -r requirements.txt or pip install -r requirements.txt. Then add the server configuration (command: uv, args pointing to the directory and run uniprot-mcp-server) to your Claude Desktop config file. Once configured, you can ask Claude to retrieve protein information by providing UniProt accession numbers.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"uniprot mcp server": {
"uniprot-mcp-server": {
"command": "uv",
"args": [
"pip",
"install",
"-r",
"requirements.txt"
]
}
}
}
}
McpServers
{
"uniprot-mcp-server": {
"command": "uv",
"args": [
"pip",
"install",
"-r",
"requirements.txt"
]
}
}
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.
<a href="https://glama.ai/mcp/servers/ttjbai3lpx">
</a>
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
- 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"
}
2. 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
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
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