io.github.Seif-Sameh/Kaggle-mcp

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

1. 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>

1. Restart Claude Desktop

1. 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.