kaggle-mcp
Provides tools for interacting with Kaggle's API, enabling AI agents to manage datasets, competitions, and other Kaggle resources.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@kaggle-mcp search for datasets about climate change"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Related MCP server: Kaggle-MCP
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
Development
Building and Publishing
To prepare the package for distribution:
Sync dependencies and update lockfile:
uv syncBuild 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_TOKENOr username/password:
--username/UV_PUBLISH_USERNAMEand--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.
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.
Available Tools
1 toolprepare_kaggle_datasetB
Download and extract a Kaggle dataset.
Inputs: competition_id (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion between tools, so disambiguation is perfect.
The single tool follows a consistent verb_noun pattern (prepare_kaggle_dataset), so naming is perfectly consistent.
Only one tool for a server named 'kaggle-mcp' is far too few; a typical Kaggle interaction requires multiple operations beyond just downloading.
The single tool covers only dataset download/extraction, missing essential CRUD operations, search, and listing, making the surface severely incomplete.
Maintenance
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