Gemini CLI RAG MCP

What it is

A server that turns the Gemini CLI's markdown documentation into a searchable index and exposes it as a single MCP tool. A build step walks the docs directory, concatenates the pages, splits them into overlapping chunks and embeds them into a vector store saved as a file in the project; at query time the tool retrieves the passages that match the question and returns them to the client.

What you get

• One tool that takes a plain-language question and returns the documentation passages that match it
• The whole documentation text published as a resource, for a client that would rather read it entire
• An index built and stored locally, from the docs directory you point the extractor at
• Local embeddings, so questions are answered without calling an outside service
• A container the client drives over stdio, so nothing is left running when it is idle

Requirements

Docker to run the container, Python — the project pins 3.13 — to build the index, and a local copy of the gemini-cli documentation for the extract step to walk. No account, no key: the embedding model and the index both run on your machine.

Setup effort

Run a container — build the image from the repository, then point your client at it

Tools you get

1 tool named in the project's documentation. Answers cover only the gemini-cli/docs checkout you indexed. docs://gemini-cli/full is a resource returning the whole docs file, not a tool.

Requires

• docker on PATH
• Python 3.13

Where it runs

Both documented clients run the server on your machine.