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dexhunter

kaggle-mcp

by dexhunter
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Python
Remote

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:

  1. Sync dependencies and update lockfile:

uv sync

  1. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

  1. Publish to PyPI:

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.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory /Users/{username}/Work/kaggle-mcp run kaggle-mcp

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

Available Tools

1 tool
prepare_kaggle_datasetB

Download and extract a Kaggle dataset.

ParametersJSON Schema
NameRequiredDescriptionDefault
competition_idYesThe Name of the Kaggle competition to download the dataset from.

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description must disclose behavioral traits. It only states 'download and extract' without mentioning potential side effects, system interactions, or limitations (e.g., requires Kaggle API, competition rules acceptance).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no extraneous words. It is efficient, though it could be more detailed. The structure is fine.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool interacts with an external service (Kaggle) and has no output schema, the description lacks critical context such as what happens after download (e.g., file location), failure handling, or quota information. It is incomplete for safe autonomous invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers the parameter 'competition_id' with 100% coverage, so baseline is 3. The description does not add extra meaning beyond 'download a Kaggle dataset', which is already implied. No additional formatting or usage tips are provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (download and extract) and the resource (Kaggle dataset). It is specific and unambiguous, fulfilling the need for a verb+resource statement.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool, prerequisites (e.g., Kaggle API authentication), or alternatives. There are no sibling tools, but context about typical use cases is missing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.1/5.0
Disambiguation5/5

With only one tool, there is no possibility of confusion between tools, so disambiguation is perfect.

Naming Consistency5/5

The single tool follows a consistent verb_noun pattern (prepare_kaggle_dataset), so naming is perfectly consistent.

Tool Count1/5

Only one tool for a server named 'kaggle-mcp' is far too few; a typical Kaggle interaction requires multiple operations beyond just downloading.

Completeness1/5

The single tool covers only dataset download/extraction, missing essential CRUD operations, search, and listing, making the surface severely incomplete.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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