kaggle-mcp

<a href="https://glama.ai/mcp/servers/arwswog1el"><img width="380" height="200" src="https://glama.ai/mcp/servers/arwswog1el/badge" alt="Kaggle MCP Server" /></a> # 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. ## Features - 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. ## Available MCP Capabilities ### Tools #### `search_kaggle_datasets(query: str)` Searches Kaggle for datasets matching `query` and returns up to 10 results as JSON. Returned fields include: - `ref` - `title` - `subtitle` - `download_count` - `last_updated` - `usability_rating` #### `download_kaggle_dataset(dataset_ref: str, download_path: str | None = None)` Downloads and unzips a Kaggle dataset. - `dataset_ref`: Kaggle dataset reference in `owner/dataset-slug` format, for example `kaggle/titanic`. - `download_path`: Optional local output path. If omitted, files are saved to `./datasets/<dataset_slug>/`. ### Prompts #### `generate_eda_notebook(dataset_ref: str)` Creates a prompt for generating basic Python EDA code for the provided Kaggle dataset reference. The prompt asks for data loading, missing-value checks, visualizations, and summary statistics. ## Requirements - Python 3.10+ - Kaggle account and API token - An MCP-compatible client ## Kaggle Credentials Create a Kaggle API token from your Kaggle account settings: 1. Go to <https://www.kaggle.com/settings>. 2. Select **Create New API Token**. 3. Download `kaggle.json`. Use either environment variables or the standard Kaggle config file. ### Option 1: Environment variables Create a `.env` file in the project root: ```dotenv KAGGLE_USERNAME=your_kaggle_username KAGGLE_KEY=your_kaggle_api_key ``` ### Option 2: `kaggle.json` Place `kaggle.json` in the standard Kaggle location: - macOS/Linux: `~/.kaggle/kaggle.json` - Windows: `C:\Users\<Your User Name>\.kaggle\kaggle.json` On macOS/Linux, make sure the file is not world-readable: ```bash chmod 600 ~/.kaggle/kaggle.json ``` ## Installation ```bash git clone <repository-url> cd kaggle-mcp ``` Create and activate a virtual environment: ```bash python -m venv .venv source .venv/bin/activate # Windows: .venv\Scripts\activate ``` Install dependencies with one of the following methods. ### Using uv ```bash uv sync ``` ### Using pip ```bash pip install -r requirements.txt ``` ## Running Locally With `uv`: ```bash uv run kaggle-mcp ``` Or run the server module directly: ```bash python src/server.py ``` The server communicates over MCP stdio and is intended to be launched by an MCP client. ## Claude Desktop Configuration Open Claude Desktop settings, then go to **Developer** > **Edit Config** and add this server to `claude_desktop_config.json`. If installed in the project environment: ```json { "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" } } } } ``` If using `kaggle.json`, you can omit the `env` block. ## Docker Build the image: ```bash docker build -t kaggle-mcp . ``` Run with credentials from `.env`: ```bash docker run --rm -i --env-file .env kaggle-mcp ``` ## Smithery This repository includes `smithery.yaml`. Smithery starts the server over stdio and passes these configuration values as environment variables: - `kaggleUsername` -> `KAGGLE_USERNAME` - `kaggleKey` -> `KAGGLE_KEY` ## Example Workflow 1. Ask your MCP client: "Search Kaggle for heart disease datasets." 2. The client calls `search_kaggle_datasets`. 3. Choose a dataset reference from the results, for example `user/heart-disease-dataset`. 4. Ask: "Download `user/heart-disease-dataset`." 5. Ask: "Generate an EDA notebook prompt for `user/heart-disease-dataset`." ## Project Structure ```text . ├── Dockerfile ├── README.md ├── pyproject.toml ├── requirements.txt ├── smithery.yaml ├── src/ │ ├── __init__.py │ └── server.py └── uv.lock ``` Downloaded datasets are saved under `datasets/` by default. This directory is created at runtime when downloads are requested.