Koog Documentation Server

A Model Context Protocol (MCP) server that provides vector-based RAG search capabilities over Koog documentation using ChromaDB.

Setup

1. Create and activate virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Clone the koog git repository and remember the path of your working copy.

4. Add to your Q CLI MCP configuration:

q config mcp add koog-docs python3 koog-mcp-server.py [koog-repo-path]

If you omit koog-repo-path, it will default to '../koog'

Or manually add to your MCP config file:

{
  "mcpServers": {
    "koog-docs": {
      "command": "python3",
      "args": ["koog-mcp-server.py"],
      "cwd": "/path/to/this/directory"
    }
  }
}

Database Initialization

The vector database will be automatically created and populated on first use:

• ChromaDB creates a persistent database in ./chroma_db/
• All markdown files from ../koog/docs/docs are processed and indexed
• Documents are chunked for optimal retrieval
• Vector embeddings are generated using ChromaDB's default model

Note: First startup may take a few moments while the documentation is indexed.

Usage

Once configured, you can ask questions about Koog in Q CLI:

• "How do I create a basic agent in Koog?"
• "What are the different types of tools available?"
• "How does memory work in Koog agents?"
• "Show me examples of structured output"

The server will search through all Koog documentation files and return relevant sections with context.

Features

• Vector-based semantic search using ChromaDB
• Automatic document chunking for better retrieval
• Persistent vector database with embeddings
• Searches all markdown files in the Koog docs directory
• Returns relevant sections with similarity scores
• Extracts document titles and file paths for reference