How do I install the Kaggle-mcp MCP server?

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "kaggle-mcp": { "command": "npx", "args": ["-y", "Kaggle-mcp"] } }

What does Kaggle-mcp do?

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

Is the Kaggle-mcp MCP server free to use?

Yes. Kaggle-mcp is listed on AllMCPs as a free, open Model Context Protocol server you can install into Claude Desktop, Cursor, or any MCP-compatible client.

How do I install the Kaggle-mcp MCP server?

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "kaggle-mcp": { "command": "npx", "args": ["-y", "Kaggle-mcp"] } }

What does Kaggle-mcp do?

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

Is the Kaggle-mcp MCP server free to use?

Yes. Kaggle-mcp is listed on AllMCPs as a free, open Model Context Protocol server you can install into Claude Desktop, Cursor, or any MCP-compatible client.

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:

bash

uvx mcp-server-kaggle

Or install it explicitly:

Terminal

pip install mcp-server-kaggle
# or
uv tool install mcp-server-kaggle

Install from Source

For development or local modifications:

bash

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)

server.ts

export KAGGLE_USERNAME=your_username
export KAGGLE_API_KEY=your_api_key

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

server.ts

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:

env

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:

config.json

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

Running from a local source clone (alternative)

config.json

{
  "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:

bash

mcp-server-kaggle

Or as a Python module:

bash

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:

Code

"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:

Code

"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:

Code

"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.

Kaggle MCP Server

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:

bash

uvx mcp-server-kaggle

Or install it explicitly:

Terminal

pip install mcp-server-kaggle
# or
uv tool install mcp-server-kaggle

Install from Source

For development or local modifications:

bash

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)

server.ts

export KAGGLE_USERNAME=your_username
export KAGGLE_API_KEY=your_api_key

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

server.ts

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:

env

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:

config.json

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

Running from a local source clone (alternative)

config.json

{
  "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:

bash

mcp-server-kaggle

Or as a Python module:

bash

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:

Code

"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:

Code

"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:

Code

"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.

Kaggle Mcp

Quick Install

Install Config Generator

Documentation Overview

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

Related MCP Servers

Frequently Asked Questions about Kaggle Mcp

AllMCPs Directory Badge

Technical Specs & Signals

Feature Your MCP Server

Own this project?

Share & Embed

Explore more

Kaggle Mcp

Quick Install

Install Config Generator

Documentation Overview

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

Related MCP Servers

Frequently Asked Questions about Kaggle Mcp

AllMCPs Directory Badge

Technical Specs & Signals

Feature Your MCP Server

Own this project?

Share & Embed

Explore more