Kaggle Mcp

io.github.Seif-SamehPublished by GitHub account Seif-Samehv0.1.1Updated Oct 27, 2026

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

VerifiedSTDIODesktop onlyData & Analytics AI & ML

Server details

Identifier: io.github.Seif-Sameh/Kaggle-mcp
Transport: STDIO
Runtime: Desktop only
Trust: Verified
Published: Sep 16, 2026

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Overview

AI-generated overview

Local MCP server for the Kaggle API that lets an assistant browse competitions, datasets, kernels and models, download files, and submit entries.

What you can do: List and search competitions, view leaderboards and submission history, download competition files and submit predictions
Search, inspect, download, create and version datasets
List, initialize, push, pull and check status of kernels and their output files
Create, update and delete models, model instances and model versions, and download model versions
When to use it: Useful when you work on Kaggle from an MCP client such as Claude Desktop and want the assistant to find competitions or datasets, pull data and notebooks locally, or manage models and submissions without switching to the Kaggle website.
Requirements: Runs locally as a Python package (Python 3.10 or higher), typically launched with uvx or pip. Needs a Kaggle account and API credentials supplied as the KAGGLE_USERNAME and KAGGLE_API_KEY environment variables (or a .env file). Network access to Kaggle is required.

Before you install

The server holds your Kaggle API credentials, which grant access to your account. Several tools write to Kaggle: competition submissions, dataset creation and versioning, kernel pushes, and model, instance and version creation or deletion. Downloads write files to your machine. Review actions before letting an assistant submit or delete anything.

Installation

In SourceWeft

Open Kaggle Mcp in the dashboard and add it to a workspace.
Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

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:

Or install it explicitly:

Install from Source

For development or local modifications:

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)

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

bash

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

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:

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:

json

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

{  "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)

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"      }    }  }}

{  "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:

Or as a Python module:

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:

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

Example 2: Downloading Datasets

Ask Claude:

Claude will use dataset_download_files to fetch all dataset files.

Example 3: Submitting to Competitions

Ask Claude:

Claude will use competition_submit to upload your submission.

License

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

Source: README.md at commit 62b5cb0

Tools

SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

v0.1.1LatestSep 16, 2026

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