What models does Godot RAG support?
It supports all-MiniLM-L6-v2, bge-m3, and bge-large-zh-v1.5 sentence transformer models.
A MCP server for Godot RAG
@weekitmo
基本情報
A MCP server for Godot RAG について
This MCP server is used to provide Godot documentation to the Godot RAG model.
A MCP server for Godot RAG の設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"mcp_godot_rag": {
"command": "uv",
"args": [
"venv",
"--python",
"3.12"
]
}
}
}A MCP server for Godot RAG のツール
ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
概要
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.
よくある質問
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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