A MCP server for Godot RAG
About
This MCP server is used to provide Godot documentation to the Godot RAG model.
Details
- Author
- weekitmo
- GitHub stars
- 27
- Downloads
- 265
- Categories
- Developer Tools, Other, Knowledge Base
Jump to
- Retrieves Godot documentation for RAG models.
- Uses ChromaDB as vector store.
- Supports multiple sentence transformer models.
- Provides setup scripts for document ingestion.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
A MCP server for Godot RAGCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"a mcp server for godot rag": {
"mcp_godot_rag": {
"command": "uv",
"args": [
"venv",
"--python",
"3.12"
]
}
}
}
}McpServers
{
"mcp_godot_rag": {
"command": "uv",
"args": [
"venv",
"--python",
"3.12"
]
}
}This MCP server is used to provide Godot documentation to the Godot RAG model.
{ "mcpServers": { "godot-rag": { "command": "python", "args": [ "<path to the server script 'main.py'>", "-d", "<path to the chroma_db on your computer>", "-c", "<name of the collection in the chroma_db>" ] } } }
uv venv --python 3.12 source ./.venv/bin/activate uv sync cp .env.example .env.local
# clone godot docs python download_godot_docs.py # convert rst to markdown python convert_rst2md.py # chunk markdown files python chunker.py -i artifacts # create vector database python vectorizer.py -i artifacts/chunks/artifacts_chunks_SZ_400_O_20.jsonl # python vectorizer_api.py -i artifacts/chunks/artifacts_chunks_SZ_400_O_20.jsonl -m BAAI/bge-m3 # start mcp server python main.py -d artifacts/vector_stores/chroma_db -c artifacts_chunks_SZ_400_O_20_all-MiniLM-L6-v2 # python main_with_api.py -d artifacts/vector_stores/chroma_db -c artifacts_chunks_SZ_400_O_20_BAAI-bge-m3 -k <your openai api key>
npx @modelcontextprotocol/inspector \ uv \ --directory . \ run \ main.py \ --chromadb-path artifacts/vector_stores/chroma_db \ --collection-name artifacts_chunks_SZ_400_O_20_all-MiniLM-L6-v2
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