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Installation

This guide covers the various ways to install and run the Docs MCP Server.

🚀 Quick Start (Recommended)

The easiest way to get started is using the standalone server, which includes both the MCP endpoints and the web interface in a single process.

Option 1: Node.js (npx)

If you have Node.js 22.x installed (recommended for local development), you can run the server directly with a single command. Use nvm use 22 and run npm rebuild if you recently changed Node versions:

npx @arabold/docs-mcp-server@latest

This runs the server on port 6280 by default. Open http://localhost:6280 to access the web interface.

Optional: Prefix with OPENAI_API_KEY="your-openai-api-key" to enable vector search for improved results.

Option 2: Docker

Running via Docker ensures you have all dependencies without polluting your host system.

docker run --rm \
  -v docs-mcp-data:/data \
  -v docs-mcp-config:/config \
  -p 6280:6280 \
  ghcr.io/arabold/docs-mcp-server:latest \
  --protocol http --host 0.0.0.0 --port 6280

Configuration: The server writes its configuration to /config/docs-mcp-server/config.yaml. Mounting the /config volume ensures your settings persist across restarts.

Optional: Add -e OPENAI_API_KEY="your-openai-api-key" to enable vector search for improved results.

Configure Your Client

Once the server is running (on port 6280), you need to tell your AI client where to find it.

Example: Cursor

Add this to your MCP settings:

{
  "mcpServers": {
    "docs-mcp-server": {
      "type": "streamableHttp",
      "url": "http://localhost:6280/mcp"
    }
  }
}

See Connecting MCP Clients for instructions for Claude, Cline, Zed, and others.

Optional: To improve search quality, see Embedding Models to configure OpenAI, Ollama, or other providers.

🔌 Embedded Server

You can run the MCP server directly embedded in your AI assistant without a separate process or web interface. This provides MCP integration only.

Add this to your MCP settings (VS Code, Claude Desktop, etc.):

{
  "mcpServers": {
    "docs-mcp-server": {
      "command": "npx",
      "args": ["@arabold/docs-mcp-server@latest"],
      "disabled": false,
      "autoApprove": []
    }
  }
}

With Vector Search (API Key):

{
  "mcpServers": {
    "docs-mcp-server": {
      "command": "npx",
      "args": ["@arabold/docs-mcp-server@latest"],
      "env": {
        "OPENAI_API_KEY": "sk-proj-..."
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Note: When running in embedded mode, you lose access to the Web Interface unless you launch it separately (see Basic Usage).

🐳 Advanced: Docker Compose (Scaling)

For production deployments or when you need to scale processing, use Docker Compose to run separate services. The system selects either a local in-process worker or a remote worker client based on the configuration.

  1. Clone the repository:

    git clone https://github.com/arabold/docs-mcp-server.git
cd docs-mcp-server

  2. Set your environment variables:

    export OPENAI_API_KEY="your-key-here"

  3. Start all services:

    docker compose up -d

Service Architecture

  • Worker (port 8080): Handles documentation processing jobs.
  • MCP Server (port 6280): Provides /sse endpoint for AI tools.
  • Web Interface (port 6281): Browser-based management interface.

See Deployment Modes for more architectural details.