Kaggle MCP (Model Context Protocol) Server
@arrismo
Kaggle MCP (Model Context Protocol) Server について
MCP server for Kaggle
基本情報
設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"kaggle-mcp": {
"command": "python",
"args": [
"-m",
"venv",
".venv"
]
}
}
}ツール
ツールは検出されませんでした
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
概要
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
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