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MCP for the open-genes
MCP (Model Context Protocol) server for OpenGenes database
This server implements the Model Context Protocol (MCP) for OpenGenes, providing a standardized interface for accessing aging and longevity research data. MCP enables AI assistants and agents to query comprehensive biomedical datasets through structured interfaces.
The server automatically downloads the latest OpenGenes database and documentation from Hugging Face Hub (specifically from the opengenes folder), ensuring you always have access to the most up-to-date data without manual file management.
The OpenGenes database contains:
If you want to understand more about what the Model Context Protocol is and how to use it more efficiently, you can take the DeepLearning AI Course or search for MCP videos on YouTube.
This MCP server is part of the Holy Bio MCP project - a unified framework for bioinformatics research that won the Bio x AI Hackathon 2025 and continues to be actively developed and extended after the victory.
The Holy Bio MCP framework brings together multiple specialized MCP servers into a cohesive ecosystem for advanced biological research:
Together, these servers provide 50+ specialized bioinformatics functions that can work seamlessly together in AI-driven research workflows. Learn more about the complete framework at github.com/longevity-genie/holy-bio-mcp.
Here's how the OpenGenes MCP server works in practice with AI assistants:
Example showing how to query the OpenGenes database through an AI assistant using natural language, which gets translated to SQL queries via the MCP server. You can use this database both in chat interfaces for research questions and in AI-based development tools (like Cursor, Windsurf, VS Code with Copilot) to significantly improve your bioinformatics productivity by having direct access to aging and longevity research data while coding.
MCP is a protocol that bridges the gap between AI systems and specialized domain knowledge. It enables:
The OpenGenes MCP server automatically downloads data from the longevity-genie/bio-mcp-data repository on Hugging Face Hub. This ensures:
The data files are stored in the opengenes subfolder of the Hugging Face repository and include:
open_genes.sqlite - The complete OpenGenes databaseprompt.txt - Database schema documentation and usage guidelinesThis server provides three main tools for interacting with the OpenGenes database:
opengenes_db_query(sql: str) - Execute read-only SQL queries against the OpenGenes databaseopengenes_get_schema_info() - Get detailed schema information including tables, columns, and enumerationsopengenes_example_queries() - Get a list of example SQL queries with descriptionsresource://db-prompt - Complete database schema documentation and usage guidelinesresource://schema-summary - Formatted summary of tables and their purposes# Download and install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
# Verify installation
uv --version
uvx --version
uvx is a very nice tool that can run a python package installing it if needed.
You can run the opengenes-mcp server directly using uvx without cloning the repository:
# Run the server in streamed http mode (default)
uvx opengenes-mcp
# Or explicitly specify stdio mode
uvx opengenes-mcp stdio
# Run the server in streamable HTTP mode on default (3001) port
uvx opengenes-mcp server
# Run on a specific port
uvx opengenes-mcp server --port 8000
# Run the server in SSE mode
uvx opengenes-mcp sse
In cases when there are problems with uvx often they can be caused by clenaing uv cache:
uv cache clean
The HTTP mode will start a web server that you can access at http://localhost:3001/mcp (with documentation at http://localhost:3001/docs). The STDIO mode is designed for MCP clients that communicate via standard input/output, while SSE mode uses Server-Sent Events for real-time communication.
Note: Currently, we do not have a Swagger/OpenAPI interface, so accessing the server directly in your browser will not show much useful information. To explore the available tools and capabilities, you should either use the MCP Inspector (see below) or connect through an MCP client to see the available tools.
Here's what you can copy directly into your Claude Desktop or Cursor MCP configuration:
{
"mcpServers": {
"opengenes-mcp": {
"command": "uvx",
"args": ["opengenes-mcp"],
"env": {
"MCP_TRANSPORT": "stdio"
}
}
}
}
We also provide preconfigured JSON files for different use cases:
mcp-config-stdio.jsonmcp-config.jsonmcp-config-stdio-debug.jsonFor a visual guide on how to configure MCP servers with AI clients, check out our configuration tutorial video for our sister MCP server (biothings-mcp). The configuration principles are exactly the same for the OpenGenes MCP server - just use the appropriate JSON configuration files provided above.
If you want to inspect the methods provided by the MCP server, use npx (you may need to install nodejs and npm):
For STDIO mode with uvx:
npx @modelcontextprotocol/inspector --config mcp-config-stdio.json --server opengenes-mcp
For HTTP mode (ensure server is running first):
npx @modelcontextprotocol/inspector --config mcp-config.json --server opengenes-mcp
For local development:
npx @modelcontextprotocol/inspector --config mcp-config-stdio-debug.json --server opengenes-mcp
You can also run the inspector manually and configure it through the interface:
npx @modelcontextprotocol/inspector
After that you can explore the tools and resources with MCP Inspector at http://127.0.0.1:6274 (note, if you run inspector several times it can change port)
Simply point your AI client (like Cursor, Windsurf, ClaudeDesktop, VS Code with Copilot, or others) to use the appropriate configuration file from the repository.
# Clone the repository
git clone https://github.com/longevity-genie/opengenes-mcp.git
cd opengenes-mcp
uv sync
If you already cloned the repo you can run the server with uv:
# Start the MCP server locally (HTTP mode)
uv run server
# Or start in STDIO mode
uv run stdio
# Or start in SSE mode
uv run sse
-- Get top genes with most lifespan experiments
SELECT HGNC, COUNT(*) as experiment_count
FROM lifespan_change
WHERE HGNC IS NOT NULL
GROUP BY HGNC
ORDER BY experiment_count DESC
LIMIT 10;
-- Find genes that increase lifespan in mice
SELECT DISTINCT HGNC, effect_on_lifespan
FROM lifespan_change
WHERE model_organism = 'mouse'
AND effect_on_lifespan = 'increases lifespan'
AND HGNC IS NOT NULL;
-- Get hallmarks of aging for genes
SELECT HGNC, "hallmarks of aging"
FROM gene_hallmarks
WHERE "hallmarks of aging" LIKE '%mitochondrial%';
-- Find longevity associations by ethnicity
SELECT HGNC, "polymorphism type", "nucleotide substitution", ethnicity
FROM longevity_associations
WHERE ethnicity LIKE '%Italian%';
-- Find genes with both lifespan effects and longevity associations
SELECT DISTINCT lc.HGNC
FROM lifespan_change lc
INNER JOIN longevity_associations la ON lc.HGNC = la.HGNC
WHERE lc.HGNC IS NOT NULL;
The MCP server is provided with comprehensive tests including LLM-as-a-judge tests that evaluate the quality of responses to complex queries. However, LLM-based tests are disabled by default in CI to save costs.
If you want to run LLM agent tests that use MCP functions with Gemini models, you need to set up a .env file with your Gemini API key:
# Create a .env file in the project root
echo "GEMINI_API_KEY=your-gemini-api-key-here" > .env
Note: The .env file and Gemini API key are only required for running LLM agent tests. All other tests and basic MCP server functionality work without any API keys.
Run tests for the MCP server:
uv run pytest -vvv -s
You can also run manual tests:
uv run python test/manual_test_questions.py
You can use MCP inspector with locally built MCP server same way as with uvx.
Note: Using the MCP Inspector is optional. Most MCP clients (like Cursor, Windsurf, etc.) will automatically display the available tools from this server once configured. However, the Inspector can be useful for detailed testing and exploration.
If you choose to use the Inspector via npx, ensure you have Node.js and npm installed. Using nvm (Node Version Manager) is recommended for managing Node.js versions.
We welcome contributions from the community! 🎉 Whether you're a researcher, developer, or enthusiast interested in aging and longevity research, there are many ways to get involved:
We especially encourage you to try our MCP server and share your feedback with us! Your experience using the server, any issues you encounter, and suggestions for improvement are incredibly valuable for making this tool better for the entire research community.
Tutorials, videos, and user stories are especially valuable to us! We're working to push the bioinformatics community toward AI adoption, and real-world examples of how researchers use our MCP servers (this one and others we develop) help demonstrate the practical benefits and encourage wider adoption.
git checkout -b feature/amazing-feature)uv run pytest)git commit -m 'Add amazing feature')git push origin feature/amazing-feature)black for formatting)Don't hesitate to open an issue for discussion! We're friendly and always happy to help newcomers get started. Your contributions help advance open science and longevity research for everyone. 🧬✨
Currently, this MCP server uses only a subset of the complete OpenGenes database. The full OpenGenes database contains additional tables and data that are not yet included in our MCP implementation. We need contributors to help extend support for the complete database! If you're interested in helping expand the database coverage, please see our Contributing section.
While we provide comprehensive tests including LLM-as-a-judge evaluations, not all test cases have been manually verified against the actual OpenGenes web interface. Some automated test results may need manual validation to ensure accuracy. Contributions to improve test coverage and validation are welcome.
This project is licensed under the MIT License.
This project is part of the Longevity Genie organization, which develops open-source AI assistants and libraries for health, genetics, and longevity research.
This server is part of the complete Holy Bio MCP framework, which includes:
The framework provides unified configuration files that enable all servers at once, making it easy to access 50+ specialized bioinformatics functions through a single setup. This award-winning project continues to evolve as a comprehensive platform for AI-driven biological research.
We are supported by:
HEALES - Healthy Life Extension Society
and
IBIMA - Institute for Biostatistics and Informatics in Medicine and Ageing Research
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