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MCP Server Setup

Drydock ships as an MCP (Model Context Protocol) server. This lets AI coding assistants call the analyzers directly as tool calls.

Install

pip install mcp

Configure Your Assistant

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "drydock": {
      "command": "python",
      "args": ["/path/to/Drydock/Tools/mcp_server.py"]
    }
  }
}

Add to .vscode/mcp.json in your workspace:

{
  "servers": {
    "drydock": {
      "command": "python",
      "args": ["/path/to/Drydock/Tools/mcp_server.py"]
    }
  }
}

Add to your MCP settings:

{
  "mcpServers": {
    "drydock": {
      "command": "python",
      "args": ["/path/to/Drydock/Tools/mcp_server.py"]
    }
  }
}

Tip

Replace /path/to/Drydock with the actual path where you cloned the repo.

Available Tools

Once configured, your AI assistant can call these tools:

Tool Ask your assistant...
drydock_context "Give me a full analysis of this project"
drydock_codemap "Map the files and functions in this codebase"
drydock_boundaries "Can I safely extract this component?"
drydock_dependencies "What does this project depend on?"
drydock_interfaces "What does each module expose publicly?"
drydock_structure "How big is this codebase?"
drydock_platforms "What tech stack is this project using?"

Example

After setup, just ask naturally:

"Analyze the boundaries of the project at /home/me/my-app"

Your assistant calls drydock_boundaries and gets back:

{
  "summary": {
    "total_source_files": 42,
    "clusters_found": 3,
    "bridge_files": 2,
    "orphan_files": 5
  },
  "extraction_suggestions": [
    {
      "name": "auth",
      "files": 8,
      "extraction_risk": "low",
      "reason": "No external dependencies - clean extraction"
    },
    {
      "name": "api",
      "files": 15,
      "extraction_risk": "medium",
      "reason": "3 external deps, 2 external dependents"
    }
  ]
}

The assistant can then reason about extraction decisions using real data.