Which integrations are available for this server?

Provides tools to query Kedro documentation and build a knowledge database, enabling retrieval-augmented generation (RAG) for assistance with the Kedro data science framework.

How do I use Kedro RAG MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Kedro RAG MCP How do I define a modular pipeline and register its nodes?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Kedro RAG MCP

Prerequisites

Python 3.8+
Claude Desktop app (Get from website)

Step 1: Clone and Set Up the Project

# Clone your repository git clone https://github.com/your-username/kedro-mcp-rag.git cd kedro-mcp-rag # Create a virtual environment (using conda) conda create -n kedro-rag python=3.12 -y conda activate kedro-rag # Or using venv python -m venv venv source venv/bin/activate

Step 2: Install Dependencies

# Install the RAG system dependencies pip install -r requirements.txt

Step 3: Set Up Kedro Documentation with llms.txt

3.1 Clone Kedro Repository (if not already done)

3.2 Update mkdocs.yml Configuration (if not already done)

The Kedro mkdocs.yml should have the llmstxt plugin configured:

plugins: # ... other plugins ... - llmstxt: markdown_description: | Kedro is an open-source Python framework for creating reproducible, maintainable, and modular data science code. # ... rest of description ... full_output: llms-full.txt sections: # ... sections configuration ...

3.3 Serve the Documentation

# In the kedro directory make serve-docs

This will:

Start the documentation server at http://127.0.0.1:8000
Generate the llms-full.txt file at http://127.0.0.1:8000/en/stable/llms-full.txt

Important: Keep this server running while using the RAG system!

Step 4: Configure Claude Desktop

4.1 Locate Claude Desktop Config

The config file location varies by OS:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

4.2 Update the Configuration

Edit claude_desktop_config.json and add your MCP server configuration:

{ "mcpServers": { "kedro-assistant": { "command": "/path/to/your/python", "args": ["/path/to/kedro-mcp-rag/kedro_mcp.py"], "env": { "PYTHONPATH": "/path/to/kedro-mcp-rag/" } } } }

Replace the paths with your actual paths. For example:

macOS with Anaconda:
{ "mcpServers": { "kedro-assistant": { "command": "/Users/YourName/anaconda3/envs/kedro-rag/bin/python", "args": ["/Users/YourName/GitHub/kedro-mcp-rag/kedro_mcp.py"], "env": { "PYTHONPATH": "/Users/YourName/GitHub/kedro-mcp-rag/" } } } }

To find the correct Python path:

# With conda environment activated which python # macOS/Linux # Or conda info --envs # Shows all conda environments

Step 5: Test the Setup

5.1 Restart Claude Desktop

Completely quit Claude Desktop
Restart Claude Desktop
The MCP tools should now be available
Ask it a kedro related question and it will use the tools to build knowledge DB at /tmp/kedro_knowledge_db if it doesn't already exist.
If you see the following in Claude Desktop settings then MCP is up and running with the RAG.