mcp-docs-rag MCP Server
RAG (Retrieval-Augmented Generation) for documents in a local directory
This is a TypeScript-based MCP server that implements a RAG system for documents stored in a local directory. It allows users to query documents using LLMs with context from locally stored repositories and text files.
Features
Resources
- List and access documents via `docs://` URIs
- Documents can be Git repositories or text files
- Plain text mime type for content access
Tools
- `list_documents` - List all available documents in the DOCS_PATH directory - Returns a formatted list of all documents - Shows total number of available documents
- `rag_query` - Query documents using RAG - Takes document_id and query as parameters - Returns AI-generated responses with context from documents
- `add_git_repository` - Clone a Git repository to the docs directory with optional sparse checkout - Takes repository_url as parameter - Optional document_name parameter to customize the name of the document (use simple descriptive names without '-docs' suffix) - Optional subdirectory parameter for sparse checkout of specific directories - Automatically pulls latest changes if repository already exists
- `add_text_file` - Download a text file to the docs directory - Takes file_url as parameter - Uses wget to download file
Prompts
- `guide_documents_usage` - Guide on how to use documents and RAG functionality - Includes list of available documents - Provides usage hints for RAG functionality
Development
Install dependencies:
Build the server:
For development with auto-rebuild:
Setup
This server requires a local directory for storing documents. By default, it uses `~/docs` but you can configure a different location with the `DOCS_PATH` environment variable.
Document Structure
The documents directory can contain:
- Git repositories (cloned directories)
- Plain text files (with .txt extension)
Each document is indexed separately using llama-index.ts with Google's Gemini embeddings.
API Keys
This server uses Google's Gemini API for document indexing and querying. You need to set your Gemini API key as an environment variable:
You can obtain a Gemini API key from the [Google AI Studio](https://makersuite.google.com/app/apikey) website. Add this key to your shell profile or include it in the environment configuration for Claude Desktop.
Installation
To use with Claude Desktop, add the server config:
On MacOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`
On Linux: `~/.config/Claude/claude_desktop_config.json`
Make sure to replace `/Users/username/docs` with the actual path to your documents directory.
Debugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector), which is available as a package script:
The Inspector will provide a URL to access debugging tools in your browser.
Usage
Once configured, you can use the server with Claude to:
- **Add documents**: ``` Add a new document from GitHub: https://github.com/username/repository ``` or with a custom document name: ``` Add GitHub repository https://github.com/username/repository-name and name it 'framework' ``` or with sparse checkout of a specific directory: ``` Add only the 'src/components' directory from https://github.com/username/repository ``` or combine custom name and sparse checkout: ``` Add the 'examples/demo' directory from https://github.com/username/large-repo and name it 'demo-app' ``` or add a text file: ``` Add this text file: https://example.com/document.txt ```
- **Query documents**: ``` What does the documentation say about X in the Y repository? ```
- **List available documents**: ``` What documents do you have access to? ```
The server will automatically handle indexing of documents for efficient retrieval.