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Kaggle-MCP: Kaggle API Integration for Claude AI

@54yyyu

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About Kaggle-MCP: Kaggle API Integration for Claude AI

Kaggle-MCP: Connect Claude AI to the Kaggle API through the Model Context Protocol (MCP), enabling competition, dataset, and kernel operations through the AI interface.

#mcp#kaggle#claude-ai#mcp-server

Basic information

Category

Data & Analytics

License

MIT

Runtime

python

Transports

stdio

Publisher

54yyyu

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "kaggle-mcp-54yyyu": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "git+https://github.com/54yyyu/kaggle-mcp.git"
      ]
    }
  }
}

Tools

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Overview

What is Kaggle-MCP?

Kaggle-MCP connects Claude AI to the Kaggle API through the Model Context Protocol (MCP), enabling competition, dataset, and kernel operations through the AI interface. It is built for data scientists, machine learning practitioners, and anyone who wants to interact with Kaggle’s resources using natural language.

How to use Kaggle-MCP?

Install via the provided one-line script (curl on macOS/Linux, powershell on Windows) or manually with pip or uv. After installation, run kaggle-mcp-setup to update your Claude Desktop configuration, or manually add the server entry to the Claude Desktop config file. Obtain Kaggle API credentials from your Kaggle account settings, place the kaggle.json file in ~/.kaggle/, and secure it with chmod 600. Then ask Claude to list competitions, download datasets, search kernels, and more.

Key features of Kaggle-MPC

  • Authentication: Securely authenticate with your Kaggle credentials
  • Competitions: Browse, search, and download data from Kaggle competitions
  • Datasets: Find, explore, and download datasets from Kaggle
  • Kernels: Search for and analyze Kaggle notebooks/kernels
  • Models: Access pre-trained models available on Kaggle

Use cases of Kaggle-MPC

  • Competition Research: Quickly access competition details, data, and leaderboards
  • Dataset Discovery: Find and download datasets for analysis projects
  • Learning Resources: Locate relevant kernels and notebooks for specific topics
  • Model Discovery: Find pre-trained models for various machine learning tasks

FAQ from Kaggle-MPC

How do I set up Kaggle API credentials?

Go to your Kaggle account settings, click “Create New API Token” to download a kaggle.json file, move it to ~/.kaggle/kaggle.json, and set permissions with chmod 600 ~/.kaggle/kaggle.json. Alternatively, authenticate directly through Claude using the authenticate() tool with your username and API key.

Where is the Claude Desktop configuration file located?

The configuration file is typically at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows, and ~/.config/Claude/claude_desktop_config.json on Linux.

What are the system requirements?

Python 3.8 or newer, Claude Desktop or API access, a Kaggle account with API credentials, and MCP Python SDK 1.6.0+.

How can I install Kaggle-MCP?

Use the one‑line installer: curl -LsSf https://raw.githubusercontent.com/54yyyu/kaggle-mcp/main/install.sh | sh on macOS/Linux or the PowerShell equivalent on Windows. You can also install manually with pip install git+https://github.com/54yyyu/kaggle-mcp.git (or with uv).

What can I do with Kaggle-MCP after setup?

You can list active competitions, view leaderboards, download datasets (e.g., Boston housing), search for kernels about sentiment analysis, find datasets about climate change, and authenticate with your username and API key—all through natural‑language commands to Claude.

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