A MCP server for kaggle apis
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# kaggle-mcp MCP server A MCP server for Kaggle Apis ## Components ### Tools The server implements one tool: - add-note: Adds a new note to the server - Takes "name" and "content" as required string arguments - Updates server state and notifies clients of resource changes ## Configuration Ensure that you have downloaded your Kaggle credentials (`kaggle.json`) and placed it in the `~/.kaggle/` directory (this is the default location where the Kaggle API looks for your credentials) Otherwise you can add the env `KAGGLE_USERNAME` and `KAGGLE_KEY` to the mcp config ## Quickstart ### Install #### Claude Desktop On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json` On Windows: `%APPDATA%/Claude/claude_desktop_config.json` <details> <summary>Development/Unpublished Servers Configuration</summary> ``` "mcpServers": { "kaggle-mcp": { "command": "uv", "args": [ "--directory", "/Users/{username}/Work/kaggle-mcp", "run", "kaggle-mcp" ] } } ``` </details> <details> <summary>Published Servers Configuration</summary> ``` "mcpServers": { "kaggle-mcp": { "command": "uvx", "args": [ "kaggle-mcp" ] } } ``` </details> ## Development ### Building and Publishing To prepare the package for distribution: 1. Sync dependencies and update lockfile: ```bash uv sync ``` 2. Build package distributions: ```bash uv build ``` This will create source and wheel distributions in the `dist/` directory. 3. Publish to PyPI: ```bash uv publish ``` Note: You'll need to set PyPI credentials via environment variables or command flags: - Token: `--token` or `UV_PUBLISH_TOKEN` - Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD` ### Debugging Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the [MCP Inspector](https://github.com/…
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