Which integrations are available for this server?

Retrieves supporting scientific literature from PubMed for genomic variants and diseases. Provides optional persistence layer using Supabase for storing genomic data and configurations.

How do I use GenomeMCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@GenomeMCP search ClinVar for BRCA1" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

GenomeMCP

AI-powered genomic intelligence through the Model Context Protocol

Python 3.10+ MCP License: MIT ClinVar gnomAD Reactome Deploy on Railway

GenomeMCP is a research-grade Model Context Protocol (MCP) server that enables AI agents to query clinical genomics databases, retrieve supporting scientific literature, analyze population genetics, and visualize biological pathways — all in real-time.

🖥️ CLI Tool

GenomeMCP includes a beautiful command-line interface with rich formatting and an interactive TUI mode.

Quick Install

# Recommended (any platform with Python)
pipx install genomemcp

# macOS (Homebrew)
brew install nexisdev/tap/genomemcp

# Windows (Scoop)
scoop bucket add genomemcp https://github.com/nexisdev/scoop-genomemcp
scoop install genomemcp

# From source
git clone https://github.com/nexisdev/GenomeMCP.git
cd GenomeMCP && ./install.sh

Standalone binaries available on GitHub Releases.

CLI Commands

genomemcp search BRCA1              # 🔍 Search ClinVar
genomemcp variant 12345             # 📋 Get variant report
genomemcp gene TP53                 # 🧬 Get gene info
genomemcp pathway EGFR --visualize  # 🔬 Pathway analysis
genomemcp population 1-55516888-G-GA # 👥 gnomAD frequencies
genomemcp discover "Lynch Syndrome" # 🔗 Discover related genes
genomemcp tui                       # 🖥️ Interactive mode

Theme Options

genomemcp --theme cyberpunk search BRCA1
genomemcp --theme professional gene TP53
genomemcp --theme minimal pathway EGFR

See CLI Guide for complete documentation.

🎯 Why GenomeMCP?

Problem	GenomeMCP Solution
AI agents lack genomic knowledge	Direct ClinVar, gnomAD, Reactome integration
No evidence for clinical claims	Auto-retrieves PubMed abstracts
Variant interpretation is complex	Population frequency + pathway context
Gene-disease links are opaque	Automatic relationship discovery

🧬 Features

Core Genomics Tools

search_clinvar(term) — Query ClinVar for genes, variants, or diseases
get_variant_report(id) — Detailed clinical significance report
get_gene_info(symbol) — Gene function, location, and aliases from NCBI Gene
get_supporting_literature(id) — PubMed articles linked to a variant

Population Genetics

get_population_stats(variant) — Allele frequency from gnomAD (Genome Aggregation Database)

Pathway Analysis

get_pathway_info(gene) — Reactome biological pathways for a gene
visualize_pathway(gene) — Generate Mermaid.js diagrams of gene-pathway relationships

Discovery & Synthesis

find_related_genes(phenotype) — Discover genes associated with a disease
get_genomic_context(gene, position) — Identify exon vs intron regions
get_discovery_evidence(phenotype) — Aggregate PubMed abstracts for AI reasoning

🚀 Quick Start

MCP Server Installation

# Clone the repository
git clone https://github.com/nexisdev/GenomeMCP.git
cd GenomeMCP

# Install dependencies with uv
uv sync

# Run the MCP server
uv run python src/main.py

CLI Installation

# Using the install script
./install.sh

# Or with pip
pip install genomemcp[cli]

# Or for development
./setup-dev.sh
source .venv/bin/activate

Claude Desktop Integration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "genomemcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/GenomeMCP",
        "run",
        "python",
        "src/main.py"
      ]
    }
  }
}


### ☁️ Cloud Deployment (Railway)

You can deploy the GenomeMCP server to the cloud with one click. It will be exposed as an SSE (Server-Sent Events) endpoint, ready for remote agents.

1. Click the **Deploy on Railway** button above.
2. Provide your `SUPABASE_URL` and `SUPABASE_KEY` (optional, for persistence).
3. Connect your agent to the deployment URL (e.g. `https://your-app.up.railway.app/sse`).

---

## 📖 Usage Examples

### Search for a Gene Variant

User: "What variants are associated with BRCA1?" Agent uses: search_clinvar("BRCA1")


### Get Population Frequency

User: "How common is the variant 1-55516888-G-GA?" Agent uses: get_population_stats("1-55516888-G-GA") → Returns gnomAD allele frequency: 0.000123 (0.01%)


### Discover Gene-Disease Relationships

User: "What genes are linked to Lynch Syndrome?" Agent uses: find_related_genes("Lynch Syndrome") → Returns: MSH2 (12 variants), MLH1 (8 variants), PMS2 (5 variants)


### Visualize Pathways

User: "Show me the pathways for TP53" Agent uses: visualize_pathway("TP53") → Returns Mermaid diagram:


```mermaid
graph TD
    TP53((TP53))
    TP53 --> P_123["Transcriptional Regulation by TP53"]
    TP53 --> P_456["Cell Cycle Checkpoints"]
    TP53 --> P_789["DNA Damage Response"]

🔬 Data Sources

Source	Description	API
ClinVar	Clinical variant interpretations	NCBI E-utilities
gnomAD	Population allele frequencies	gnomAD GraphQL
Reactome	Biological pathway database	Reactome Content Service
PubMed	Scientific literature	NCBI E-utilities
NCBI Gene	Gene annotations	NCBI E-utilities

🏗️ Architecture

GenomeMCP/
├── src/
│   ├── main.py          # MCP server & tool definitions
│   ├── clinvar.py       # ClinVar & PubMed API client
│   ├── genomics.py      # Exon/Intron mapping
│   ├── population.py    # gnomAD integration
│   ├── pathways.py      # Reactome integration
│   ├── utils.py         # Shared utilities
│   └── cli/             # Command-line interface
│       ├── app.py       # Typer CLI application
│       ├── formatters/  # Rich output formatters
│       ├── tui/         # Textual interactive UI
│       └── config.py    # Theme configuration
├── tests/               # Unit tests
├── docs/                # Documentation
├── install.sh           # Quick install script
├── setup-dev.sh         # Development setup
└── pyproject.toml       # Project configuration

🧪 Testing

# Run all tests
uv run pytest

# Run CLI tests
uv run pytest tests/test_cli.py -v

# Run specific test suite
uv run pytest tests/test_phase4.py tests/test_phase5.py

📚 Documentation

CLI Guide — Command-line interface documentation
Tool Reference — Complete API documentation
Architecture Guide — System design

🤝 Contributing

Contributions are welcome! Please open an issue or submit a pull request.

📄 License

MIT License — see LICENSE for details.

🔗 Keywords

genomics bioinformatics clinvar gnomad mcp model-context-protocol ai-agent claude variant-interpretation population-genetics reactome pathway-analysis pubmed ncbi gene-discovery clinical-genomics precision-medicine llm-tools cli tui terminal

GenomeMCP

GenomeMCP

🖥️ CLI Tool

Quick Install

CLI Commands

Theme Options

🎯 Why GenomeMCP?

🧬 Features

Core Genomics Tools

Population Genetics

Pathway Analysis

Discovery & Synthesis

🚀 Quick Start

MCP Server Installation

CLI Installation

Claude Desktop Integration

🔬 Data Sources

🏗️ Architecture

🧪 Testing

📚 Documentation

🤝 Contributing

📄 License

🔗 Keywords

Resources

Latest Blog Posts

MCP directory API