What is Kaggle Mcp

Kaggle-MCP is an integration tool that connects Claude AI to the Kaggle API using the Model Context Protocol (MCP). It allows users to perform operations related to competitions, datasets, and kernels directly through Claude AI’s interface.

Use cases

Kaggle-MCP is useful for quickly accessing competition details, discovering datasets for analysis projects, finding relevant learning resources through kernels and notebooks, and identifying pre-trained models for various machine learning tasks.

How to use

Users can install Kaggle-MCP via command line on macOS, Linux, or Windows. Once installed, they need to configure the setup utility or manually update their Claude Desktop configuration. Kaggle API credentials must be set up to enable functionality, either through a downloaded credentials file or through direct authentication in Claude.

Key features

Kaggle-MCP offers secure authentication with Kaggle, the ability to browse and download data from competitions, find and explore datasets, search for kernels, and access pre-trained models available on Kaggle.

Where to use

Kaggle-MCP is applicable in environments where Claude AI is used, particularly in data science, machine learning projects, or any scenario requiring access to Kaggle’s resources for competitions, datasets, and educational notebooks.

Kaggle-MCP: Kaggle API Integration for Claude AI

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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

# Install with a single command
curl -LsSf https://raw.githubusercontent.com/54yyyu/kaggle-mcp/main/install.sh | sh

Windows

# Download and run the installer
powershell -c "Invoke-WebRequest -Uri https://raw.githubusercontent.com/54yyyu/kaggle-mcp/main/install.ps1 -OutFile install.ps1; .\install.ps1"

Manual Installation

# Install with pip
pip install git+https://github.com/54yyyu/kaggle-mcp.git

# Or better, install with uv
uv pip install git+https://github.com/54yyyu/kaggle-mcp.git

Configuration

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

kaggle-mcp-setup

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:

{
  "mcpServers": {
    "kaggle": {
      "command": "kaggle-mcp"
    }
  }
}

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.