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Genome MCP Server

Provides gene data queries, cross-species homology analysis, and evolution insights via MCP with multiple transport modes.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "gqy20-genome-mcp": {
      "command": "uvx",
      "args": [
        "genome-mcp"
      ],
      "env": {
        "GENOME_MCP_LOG_LEVEL": "info"
      }
    }
  }
}

Genome MCP is a specialized server that exposes high-quality gene information, cross-species homology analysis, and evolutionary insights via the MCP protocol. It enables efficient, asynchronous queries against authoritative genomic databases and supports multiple transport modes for flexible integration with your tooling.

How to use

You connect to Genome MCP with an MCP client and run the server locally or access a remote endpoint. You can perform fast gene information lookups, retrieve homologs across species, and run evolutionary analyses. The system supports semantic search, batch processing, and multiple transport modes to fit your workflow. Use the client to issue requests such as querying a gene symbol, finding homologs for a gene, or requesting pathway enrichment analyses.

Typical usage patterns include configuring a local server instance, starting it with your preferred client, and then issuing structured requests to retrieve data, run advanced queries, or perform enrichment analyses. You can tailor the client behavior with environment variables and by selecting the transport mode that best matches your environment, such as stdio for a tightly integrated local process or HTTP for remote access.

How to install

Prerequisites
- Python 3.11 or newer
- Optional: Node-based or Python-based MCP clients (uvx, uv, npx, etc.)

Installation steps
- Install the MCP package for the server
- Run the server via your preferred client launcher

Additional configuration and usage notes

The server provides multiple ready-to-run MCP configurations. You can start and test with different launch commands to suit your workflow. Common options include starting the server through uvx, uv, or a direct Python invocation, each configured to run the genome_mcp module.

Environment variables may be used to adjust logging levels or runtime behavior. If you enable verbose logging for debugging, you can set the appropriate environment variable in your client configuration.

Examples and capabilities

{
  "mcpServers": {
    "genome-mcp": {
      "command": "uvx",
      "args": ["genome-mcp"],
      "env": {}
    }
  }
}

You can also start the server with alternative commands if you prefer a different launcher, such as running the module directly via Python or using a run command that wraps the genome_mcp module.

Security and reliability notes

Genome MCP relies on authoritative databases for gene information and homology data. Ensure you follow best practices for credentials and access controls when exposing the server in a shared environment. The system is designed for asynchronous, high-performance queries and batch processing, with robust error handling to keep analyses reliable.

Troubleshooting

If you encounter startup issues, verify that the selected launcher command is available in your environment (for example, uvx or uv), and confirm that the genome_mcp module is accessible to the launcher. Check that any required environment variables are set, and review log output to identify misconfigurations or missing dependencies.

Available tools

get_data

Fetch gene information, regional searches, and homolog queries with automatic query optimization and batch support.

advanced_query

Perform complex batch queries with customizable output formats and aggregated results.

smart_search

Semantic search that understands natural language queries and returns context-aware results.

kegg_pathway_enrichment_tool

Enrich gene lists for KEGG pathways with statistical testing and multiple organism support.