A MCP server for Kaggle Apis
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
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
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Development/Unpublished Servers Configuration
``` "mcpServers": { "kaggle-mcp": { "command": "uv", "args": [ "--directory", "/Users/{username}/Work/kaggle-mcp", "run", "kaggle-mcp" ] } } ```Published Servers Configuration
``` "mcpServers": { "kaggle-mcp": { "command": "uvx", "args": [ "kaggle-mcp" ] } } ```To prepare the package for distribution:
- Sync dependencies and update lockfile:
uv sync- Build package distributions:
uv buildThis will create source and wheel distributions in the dist/ directory.
- Publish to PyPI:
uv publishNote: You'll need to set PyPI credentials via environment variables or command flags:
- Token:
--tokenorUV_PUBLISH_TOKEN - Or username/password:
--username/UV_PUBLISH_USERNAMEand--password/UV_PUBLISH_PASSWORD
Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.
You can launch the MCP Inspector via npm with this command:
npx @modelcontextprotocol/inspector uv --directory /Users/{username}/Work/kaggle-mcp run kaggle-mcpUpon launching, the Inspector will display a URL that you can access in your browser to begin debugging.