How do I install Kaggle?

Kaggle is a local plugin. Install it using PyPI package: mcp-server-kaggle and add the generated configuration snippet to your AI app's MCP config file. Then restart your AI app.

What credentials does Kaggle need?

Kaggle requires the following credentials or environment variables: KAGGLE_USERNAME, KAGGLE_API_KEY. You can find setup instructions on the server detail page.

What AI apps work with Kaggle?

Kaggle uses the Model Context Protocol (MCP) and works with any MCP-compatible AI app, including Claude, ChatGPT / Codex, Gemini, Copilot, Cursor, and more.

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Kaggle MCP Server

by Seif Sameh

Developer ToolsUse Caution3.2MCP RegistryLocal

Free

Server data from the Official MCP Registry

MCP server for the Kaggle API: competitions, datasets, kernels, and models.

About

MCP server for the Kaggle API: competitions, datasets, kernels, and models.

Security Report

3.2

Use Caution3.2High Risk

This Kaggle MCP server has reasonable authentication and permission handling, but several code quality and security concerns lower the score. The server properly requires Kaggle API credentials via environment variables and implements appropriate error handling in most tools. However, there are critical issues: the `init_gaggle()` function writes credentials to disk in plaintext with insufficient access controls, missing input validation on several file path parameters enables potential path traversal, inconsistent async/sync function usage creates unnecessary complexity, and broad exception catching masks real errors. While permissions are appropriate for the server's purpose, the credential handling vulnerability and path traversal risks are significant. Supply chain analysis found 6 known vulnerabilities in dependencies (1 critical, 3 high severity). Package verification found 1 issue.

3 files analyzed · 15 issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

env_vars

Check that this permission is expected for this type of plugin.

File System Read

Reads files on your machine. Normal for tools that analyze or process local data.

File System Write

Writes or modifies files on your machine. Check that this is expected for the tool.

HTTP Network Access

Connects to external APIs or services over the internet.

process_spawn

Check that this permission is expected for this type of plugin.

What You'll Need

Set these up before or after installing:

Your Kaggle account usernameOptional

Environment variable: KAGGLE_USERNAME

Your Kaggle API key (from https://www.kaggle.com/account)Required

Environment variable: KAGGLE_API_KEY

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "io-github-seif-sameh-kaggle-mcp": {
      "env": {
        "KAGGLE_API_KEY": "your-kaggle-api-key-here",
        "KAGGLE_USERNAME": "your-kaggle-username-here"
      },
      "args": [
        "mcp-server-kaggle"
      ],
      "command": "uvx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

Kaggle MCP Server

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

Go to https://www.kaggle.com/account
Scroll to the "API" section
Click "Create New Token"
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.

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
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"
      }
    }
  }
}

Restart Claude Desktop
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.

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Kaggle MCP Server

About

Security Report

Findings (15)Action required

Permissions Required

What You'll Need

How to Install

Documentation

Kaggle MCP Server

Features

Installation

Prerequisites

Install from PyPI

Install from Source

Setup

1. Get Your Kaggle API Credentials

2. Configure Credentials

Usage

With Claude Desktop

Standalone Usage

Available Tools

Competitions (8 tools)

Datasets (10 tools)

Kernels (7 tools)

Models (14 tools)

Examples

Example 1: Working with Competitions

Example 2: Downloading Datasets

Example 3: Submitting to Competitions

License

Reviews

No reviews yet

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