GenomeMCP

GenomeMCP

AI-powered genomic intelligence through the Model Context Protocol

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?

🧬 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

🏗️ 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

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