iflow-mcp_arrismo-kaggle-mcp

[](https://smithery.ai/server/@arrismo/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 (Model Context Protocol) Server This repository contains an MCP (Model Context Protocol) server (`server.py`) built using the `fastmcp` library. It interacts with the Kaggle API to provide tools for searching and downloading datasets, and a prompt for generating EDA notebooks. ## Project Structure - `server.py`: The FastMCP server application. It defines resources, tools, and prompts for interacting with Kaggle. - `.env.example`: An example file for environment variables (Kaggle API credentials). Rename to `.env` and fill in your details. - `requirements.txt`: Lists the necessary Python packages. - `pyproject.toml` & `uv.lock`: Project metadata and locked dependencies for `uv` package manager. - `datasets/`: Default directory where downloaded Kaggle datasets will be stored. ## Setup 1. **Clone the repository:** ```bash git clone <repository-url> cd <repository-directory> ``` 2. **Create a virtual environment (recommended):** ```bash python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate` # Or use uv: uv venv ``` 3. **Install dependencies:** Using pip: ```bash pip install -r requirements.txt ``` Or using uv: ```bash uv sync ``` 4. **Set up Kaggle API credentials:** - **Method 1 (Recommended): Environment Variables** - Create `.env` file - Open the `.env` file and add your Kaggle username and API key: ```dotenv KAGGLE_USERNAME=your_kaggle_username KAGGLE_KEY=your_kaggle_api_key ``` - You can obtain your API key from your Kaggle account page (`Account` > `API` > `Create New API Tok…