🧬 DeepMap MCP

A high-performance Model Context Protocol (MCP) server that provides Large Language Models (LLMs) with direct, structured access to the Broad Institute's DepMap (Cancer Dependency Map) data.

Specifically designed for "Therapeutic Target Evidence Collection," this server enables agents to perform functional genomics analysis across 1,000+ cancer cell lines using the latest CRISPR (DepMap 25Q3) datasets.

🏗️ Architecture & Workflow

The server acts as a low-latency bridge between the raw DepMap CSV datasets and any MCP-compatible client (like Claude Desktop or the Agent4Target orchestrator).

graph TD
    User([User / LLM Client]) <-->|MCP Protocol| Server[DeepMap MCP Server]
    
    subgraph "Internal Engine"
        Server --> Router{Tool Router}
        Router --> S1[search_depmap_gene]
        Router --> S2[get_depmap_gene_dependency_summary]
        Router --> S3[get_depmap_top_cell_line_dependencies]
        
        S1 & S2 & S3 --> Client[DepMapClient Singleton]
        Client --> Cache[(Local CSV Cache)]
        Cache -.->|Initial Setup| Download[DepMap API]
    end
    
    subgraph "Data Output"
        Client --> Pydantic[Pydantic Result Models]
        Pydantic --> Server
    end

🚀 Quick Start

1. Installation

pip install deepmap-mcp

2. Data Initialization (Mandatory)

DeepMap datasets are large (~300MB). Run the included downloader to fetch the latest CRISPR gene effect file to your local cache:

deepmap-mcp-download-data

3. Running the Server

You can run the server over stdio (standard for Claude Desktop) or HTTP SSE:

# Run over stdio (Standard)
deepmap-mcp --transport stdio

# Run as local HTTP server (SSE)
deepmap-mcp --port 8001

🛠️ Tools Documentation

search_depmap_gene

Search for valid HGNC gene symbols within the DepMap dataset.

• Inputs: query (string, e.g., "KR")

• Use Case: Resolving partial symbols or verifying if a gene exists in the screen.

get_depmap_gene_dependency_summary

Retrieve global statistical metrics for a specific gene across the entire cell line compendium.

• Inputs: gene_symbol (string, e.g., "EGFR")

• Returns: Meta-analysis including average_gene_effect, strong_dependency_count, and confidence_score.

get_depmap_top_cell_line_dependencies

Identify the specific cancer models most sensitive to the loss of the target gene.

• Inputs: gene_symbol, top_n (default: 15)

• Use Case: Critical for Section 3 of Target Evidence Reports.

get_depmap_dataset_metadata

Returns provenance data about the local cache.

• Use Case: Ensuring reports cite the correct data release (e.g., "DepMap 25Q3").

⚙️ Configuration

Set these environment variables to customize behavior:

🖥️ Usage in Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "deepmap-mcp": {
      "command": "deepmap-mcp",
      "args": ["--transport", "stdio"],
      "env": {
        "DEEPMAP_MCP_DATASET_PATH": "/YOUR/ABSOLUTE/PATH/TO/CRISPRGeneEffect.csv"
      }
    }
  }
}

🧪 Development

Contributions are welcome!

# Clone and setup
git clone https://github.com/your-org/deepmap-mcp
cd deepmap-mcp
uv sync

# Run tests
uv run pytest

📄 License

MIT © 2026 DeepMap MCP Contributors.