About A MCP server for Godot RAG
An MCP server that provides Godot documentation to a Godot RAG model. The project includes setup scripts that download the docs, convert RST to Markdown, chunk the files and build a vector database, aimed at Godot developers and RAG experiments.
Key features
- 01Provides Godot documentation content
- 02Converts RST files to Markdown
- 03Scripts for chunking and vectorizing docs
- 04Supports alternative embedding models
- 05Debuggable with MCP Inspector
Use cases
- 01Retrieving documentation for Godot questions
- 02Building a local vector database of Godot docs
- 03Comparing embedding models for retrieval
MCP support
The project runs as an MCP server that an MCP client launches through a JSON config, supplying Godot documentation to the RAG model.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEA MCP server for Godot RAG
This MCP server is used to provide Godot documentation to the Godot RAG model.
Screenshot
Before using
After using
MCP server config
{
"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>"
]
}
}
}
Setup
uv venv --python 3.12
source ./.venv/bin/activate
uv sync
cp .env.example .env.local
Initiation steps
# 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>
Debug
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
Use Another Model
Other
mcp_godot_rag is indexed and certified by MCP Review
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.