Kaggle-MCP: Kaggle API Integration for Claude AI

Kaggle-MCP connects Claude AI to the Kaggle API through the Model Context Protocol (MCP), enabling competition, dataset, and kernel operations through the AI interface.

Features

• Authentication: Securely authenticate with your Kaggle credentials

• Competitions: Browse, search, and download data from Kaggle competitions

• Datasets: Find, explore, and download datasets from Kaggle

• Kernels: Search for and analyze Kaggle notebooks/kernels

• Models: Access pre-trained models available on Kaggle

Quick Installation

The following commands install the base version of Kaggle-MCP.

macOS / Linux

Windows

Manual Installation

Configuration

After installation, run the setup utility to configure Claude Desktop:

This will locate and update your Claude Desktop configuration file, which is typically found at:

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

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• Linux: ~/.config/Claude/claude_desktop_config.json

Manual Configuration

Alternatively, you can manually add the following to your Claude Desktop configuration:

Kaggle API Credentials

To use Kaggle-MCP, you need to set up your Kaggle API credentials:

1. Go to your Kaggle account settings

2. In the API section, click "Create New API Token"

3. This will download a kaggle.json file with your credentials

4. Move this file to ~/.kaggle/kaggle.json (create the directory if needed)

5. Set the correct permissions: chmod 600 ~/.kaggle/kaggle.json

Alternatively, you can authenticate directly through Claude using the authenticate() tool with your username and API key.

Available Tools

For a comprehensive list of available tools and their detailed usage, please refer to the documentation at stevenyuyy.us/kaggle-mcp.

Examples

Ask Claude:

• "Authenticate with Kaggle using my username 'username' and key 'apikey'"

• "List active Kaggle competitions"

• "Show me the top 10 competitors on the Titanic leaderboard"

• "Find datasets about climate change"

• "Download the Boston housing dataset"

• "Search for kernels about sentiment analysis"

Use Cases

• Competition Research: Quickly access competition details, data, and leaderboards

• Dataset Discovery: Find and download datasets for analysis projects

• Learning Resources: Locate relevant kernels and notebooks for specific topics

• Model Discovery: Find pre-trained models for various machine learning tasks

Requirements

• Python 3.8 or newer

• Claude Desktop or API access

• Kaggle account with API credentials

• MCP Python SDK 1.6.0+

License

This project is licensed under the MIT License - see the LICENSE file for details.