Kaggle MCP Server

A Model Context Protocol (MCP) server that exposes Kaggle dataset search, download, and EDA prompt generation to MCP clients such as Claude Desktop.

Local serverstdioPython

What is the Kaggle MCP MCP server?

Kaggle MCP MCP server exists for a simple reason — assistants are far more useful when they can act on Kaggle MCP directly instead of describing what you should do. A Model Context Protocol (MCP) server that exposes Kaggle dataset search, download, and EDA prompt generation to MCP clients such as Claude Desktop.

What you get

  • Search Kaggle datasets by keyword
  • Download and unzip Kaggle datasets locally
  • Generate a starter Exploratory Data Analysis (EDA) prompt for a Kaggle dataset
  • Supports Kaggle credentials via environment variables or the standard kaggle.json file
  • Runs locally, in Docker, or through Smithery

What the assistant can call

Once Kaggle MCP is connected, these are the calls the assistant has available:

  • Tools — The Tools tool exposed by this server
  • Prompts — The Prompts tool exposed by this server

Setting it up

Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.

Configuration and credentials

You will need 2 environment variables: KAGGLE_USERNAME, KAGGLE_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.

  • Python 3.10+ - Kaggle account and API token - An MCP-compatible client

Choosing this one

Plenty of cloud and infrastructure servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Kaggle MCP's toolset — Tools, Prompts — is a fair guide to whether it matches your workflow. It is maintained by arrismo; worth a glance at recent repository activity before you build anything load-bearing on it.

We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.

Before you rely on it

  • It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
  • Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
  • MCP clients confirm each tool call by default. Leave that on until you have watched what the kaggle mcp mcp server does with a few real requests.

Available tools

ToolWhat it does
ToolsThe Tools tool exposed by this server.
PromptsThe Prompts tool exposed by this server.

How to install the Kaggle MCP MCP server

{
  "mcpServers": {
    "kaggle-mcp": {
      "command": "uv",
      "args": ["run", "kaggle-mcp"],
      "cwd": "/absolute/path/to/kaggle-mcp",
      "env": {
        "KAGGLE_USERNAME": "your_kaggle_username",
        "KAGGLE_KEY": "your_kaggle_api_key"
      }
    }
  }
}

Configuration as documented by the project. Restart the client after saving.

Configuration

  • Python 3.10+ - Kaggle account and API token - An MCP-compatible client
VariableDescriptionRequired
KAGGLE_USERNAMEConfiguration value read at startup.Optional
KAGGLE_KEYCredential the server authenticates with.Yes

Example prompts to try

  • Use Kaggle MCP to Tools.
  • Use Kaggle MCP to Prompts.

Frequently asked questions

It connects Kaggle MCP to MCP-compatible AI assistants such as Claude and Cursor, exposing 2 tools (Tools, Prompts) that the assistant can call on your behalf. Instead of copying data back and forth by hand, the assistant works with Kaggle MCP directly.