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Kaggle MCP (MCP) Server

@arrismo

代码仓库

基本信息

分类

数据与分析

许可证

MIT

传输方式

stdio

作者

arrismo

关于 Kaggle MCP (MCP) Server

MCP server for Kaggle

#kaggle#mcp-server

Kaggle MCP (MCP) Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Kaggle MCP (MCP) Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.

常见问题

1

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).

2

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).

3

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).

4

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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