A MCP server for Godot RAG

@weekitmo

Basic information

About A MCP server for Godot RAG

This MCP server is used to provide Godot documentation to the Godot RAG model.

A MCP server for Godot RAG config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp_godot_rag": {
      "command": "uv",
      "args": [
        "venv",
        "--python",
        "3.12"
      ]
    }
  }
}

A MCP server for Godot RAG tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Godot RAG?

Godot RAG is an MCP server that provides Godot documentation to a Godot RAG (Retrieval-Augmented Generation) model. It is intended for developers using AI assistants to access Godot documentation.

How to use Godot RAG?

Configure the MCP client with the server command python and arguments pointing to main.py, the chroma_db path (-d), and the collection name (-c). Run the setup steps: clone Godot docs, convert RST to Markdown, chunk the files, create a vector database, then start the server.

Key features of Godot RAG

Retrieves Godot documentation for RAG models.
Uses ChromaDB as vector store.
Supports multiple sentence transformer models.
Provides setup scripts for document ingestion.

Use cases of Godot RAG

Enhance AI coding assistants with Godot documentation.
Build a custom Q&A bot for Godot engine queries.
Provide context-aware answers during game development.

Frequently asked questions

What models does Godot RAG support?

It supports all-MiniLM-L6-v2, bge-m3, and bge-large-zh-v1.5 sentence transformer models.

How do I set up the vector database?

Run `python download_godot_docs.py`, then `convert_rst2md.py`, then `chunker.py`, then `vectorizer.py` with the appropriate arguments.

What are the runtime requirements?

The server requires Python 3.12, uv for virtual environment, and dependencies from `uv sync`.

Where is the vector data stored?

The chroma_db is stored at a path specified by the `-d` argument when starting the server.

How do I debug the server?

Use `npx @modelcontextprotocol/inspector` with the `uv run main.py` command and the chromadb path and collection name arguments.

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