Kaggle MCP (Model Context Protocol) Server

Name: Kaggle MCP (Model Context Protocol) Server
Author: arrismo

@arrismo

Repository

About Kaggle MCP (Model Context Protocol) Server

MCP server for Kaggle

#kaggle #mcp-server

Basic information

Config

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

{
  "mcpServers": {
    "kaggle-mcp": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

Tools

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Overview

What is Kaggle MCP (Model Context Protocol) Server?

It is a Model Context Protocol (MCP) server that exposes Kaggle dataset search, download, and EDA prompt generation to MCP clients such as Claude Desktop. It is intended for data scientists and developers who want to interact with Kaggle datasets through AI assistants.

How to use Kaggle MCP (Model Context Protocol) Server?

Install dependencies using uv sync or pip install -r requirements.txt, set up Kaggle credentials via environment variables or kaggle.json, then run the server with uv run kaggle-mcp or python src/server.py. Configure your MCP client (e.g., Claude Desktop) by adding the server to its config file with the appropriate command and environment variables.

Key features of Kaggle MCP (Model Context Protocol) Server

Search Kaggle datasets by keyword.
Download and unzip Kaggle datasets locally.
Generate a starter exploratory data analysis (EDA) prompt.
Supports Kaggle credentials via environment variables or kaggle.json.
Runs locally, in Docker, or through Smithery.
Communicates over MCP stdio with clients like Claude Desktop.

Use cases of Kaggle MCP (Model Context Protocol) Server

Search for Kaggle datasets by keyword from an AI assistant.
Download and unzip a specific dataset to a local directory.
Generate an EDA notebook prompt to kickstart data analysis.
Integrate Kaggle dataset workflows into a Claude Desktop session.

FAQ from Kaggle MCP (Model Context Protocol) Server

How do I set up Kaggle credentials?

Use either environment variables (KAGGLE_USERNAME and KAGGLE_KEY) in a .env file, or place your kaggle.json file in the standard Kaggle location (~/.kaggle/kaggle.json on macOS/Linux).

What are the system requirements?

Python 3.10 or higher, a Kaggle account with an API token, and an MCP-compatible client (e.g., Claude Desktop).

How do I run the server in Docker?

Build the image with docker build -t kaggle-mcp . and run it with docker run --rm -i --env-file .env kaggle-mcp (using a .env file with your credentials).

How do I configure the server for Claude Desktop?

Add the server to claude_desktop_config.json under mcpServers with the command

Comments

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