Kaggle Mcp

Kaggle MCP Server

[PyPI] [MCP Registry] [License: MIT]

A Model Context Protocol (MCP) server that provides seamless integration with the Kaggle API. Interact with Kaggle competitions, datasets, kernels, and models through MCP-compatible clients like Claude Desktop.

Features

• Competitions: List, download files, submit, view leaderboards and submissions
• Datasets: Search, download, create, and manage datasets with version control
• Kernels: List, push, pull, and manage Kaggle notebooks and scripts
• Models: Create, update, and manage ML models and instances with full version control

Installation

Prerequisites

• Python 3.10 or higher
• A Kaggle account with API credentials

Install from PyPI

The recommended way is to run the server with uvx, which handles the install for you:

uvx mcp-server-kaggle

Or install it explicitly:

pip install mcp-server-kaggle# oruv tool install mcp-server-kaggle

Install from Source

For development or local modifications:

git clone https://github.com/Seif-Sameh/Kaggle-mcp.gitcd Kaggle-mcpuv sync

Setup

1. Get Your Kaggle API Credentials

1. Go to https://www.kaggle.com/account
2. Scroll to the "API" section
3. Click "Create New Token"
4. This downloads kaggle.json with your credentials

2. Configure Credentials

Option A: Environment Variables (Recommended)

export KAGGLE_USERNAME=your_usernameexport KAGGLE_API_KEY=your_api_key

Or add to your ~/.zshrc or ~/.bashrc:

echo 'export KAGGLE_USERNAME=your_username' >> ~/.zshrcecho 'export KAGGLE_API_KEY=your_api_key' >> ~/.zshrcsource ~/.zshrc

Option B: Using .env File

Create a .env file in your project directory:

KAGGLE_USERNAME=your_usernameKAGGLE_API_KEY=your_api_key

Usage

With Claude Desktop

The recommended way to use Kaggle MCP is with Claude Desktop.

1. Locate your Claude Desktop config file:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
   • Linux: ~/.config/Claude/claude_desktop_config.json

2. Add the Kaggle MCP server configuration:

{  "mcpServers": {    "kaggle": {      "command": "uvx",      "args": ["mcp-server-kaggle"],      "env": {        "KAGGLE_USERNAME": "YOUR_KAGGLE_USERNAME",        "KAGGLE_API_KEY": "YOUR_KAGGLE_API_KEY"      }    }  }}

{  "mcpServers": {    "kaggle": {      "command": "uv",      "args": [        "--directory",        "/ABSOLUTE/PATH/TO/Kaggle-mcp",        "run",        "mcp-server-kaggle"      ],      "env": {        "KAGGLE_USERNAME": "YOUR_KAGGLE_USERNAME",        "KAGGLE_API_KEY": "YOUR_KAGGLE_API_KEY"      }    }  }}

3. Restart Claude Desktop

4. Start using Kaggle through Claude!

Try asking Claude:

• "List the latest Kaggle competitions"
• "Download the Titanic dataset"
• "Show me my recent competition submissions"
• "Search for NLP datasets"

Standalone Usage

Run the MCP server directly:

mcp-server-kaggle

Or as a Python module:

python -m kaggle_mcp

Available Tools

Competitions (8 tools)

Datasets (10 tools)

Kernels (7 tools)

Models (14 tools)

Examples

Example 1: Working with Competitions

Ask Claude:

"List active Kaggle competitions about computer vision"

Claude will use the competitions_list tool to search and display relevant competitions.

Example 2: Downloading Datasets

Ask Claude:

"Download the Titanic dataset to my Downloads folder"

Claude will use dataset_download_files to fetch all dataset files.

Example 3: Submitting to Competitions

Ask Claude:

"Submit my predictions.csv to the Titanic competition with the message 'Initial baseline model'"

Claude will use competition_submit to upload your submission.

License

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