Kaggle MCP Server

<!-- mcp-name: io.github.Seif-Sameh/Kaggle-mcp -->

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
# or
uv tool install mcp-server-kaggle

Install from Source

For development or local modifications:

git clone https://github.com/Seif-Sameh/Kaggle-mcp.git
cd Kaggle-mcp
uv 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_username
export KAGGLE_API_KEY=your_api_key

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

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

Option B: Using .env File

Create a .env file in your project directory:

KAGGLE_USERNAME=your_username
KAGGLE_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"
      }
    }
  }
}

<details> <summary>Running from a local source clone (alternative)</summary>

{
  "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"
      }
    }
  }
}

</details>

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)

Tool	Description
competitions_list	List and search available competitions
competition_list_files	List all files in a competition
competition_download_file	Download a specific competition file
competition_download_files	Download all competition files
competition_submit	Submit predictions to a competition
competition_submissions	View your submission history
competition_leaderboard_view	View the competition leaderboard
competition_leaderboard_download	Download leaderboard data

Datasets (10 tools)

Tool	Description
datasets_list	Search and filter datasets
dataset_metadata	Get dataset metadata
dataset_list_files	List files in a dataset
dataset_status	Check dataset processing status
dataset_download_file	Download a specific dataset file
dataset_download_files	Download all dataset files
dataset_create	Create a new dataset
dataset_initialize	Initialize dataset metadata
dataset_create_version	Create a new dataset version

Kernels (7 tools)

Tool	Description
kernels_list	Search and filter kernels
kernel_list_files	List files in a kernel
kernel_initialize	Initialize kernel metadata
kernel_push	Push a kernel to Kaggle
kernel_pull	Download a kernel
kernel_output	Download kernel output files
kernel_status	Check kernel execution status

Models (14 tools)

Tool	Description
models_list	Search and filter models
model_get	Get model details and metadata
model_initialize	Initialize model metadata
model_create	Create a new model
model_update	Update model information
model_delete	Delete a model
model_instance_get	Get model instance details
model_instance_initialize	Initialize model instance metadata
model_instance_create	Create a new model instance
model_instance_update	Update a model instance
model_instance_delete	Delete a model instance
model_instance_version_create	Create a new model version
model_instance_version_download	Download a model version
model_instance_version_delete	Delete a model version

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.