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kaggle-mcp

by arrismo
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Kaggle MCP Server

A Model Context Protocol (MCP) server that exposes Kaggle dataset search, download, and EDA prompt generation to MCP clients such as Claude Desktop.

Features

  • Search Kaggle datasets by keyword.

  • Download and unzip Kaggle datasets locally.

  • Generate a starter Exploratory Data Analysis (EDA) prompt for a Kaggle dataset.

  • Supports Kaggle credentials via environment variables or the standard kaggle.json file.

  • Runs locally, in Docker, or through Smithery.

Related MCP server: Kaggle-MCP

Available MCP Capabilities

Tools

search_kaggle_datasets(query: str)

Searches Kaggle for datasets matching query and returns up to 10 results as JSON.

Returned fields include:

  • ref

  • title

  • subtitle

  • download_count

  • last_updated

  • usability_rating

download_kaggle_dataset(dataset_ref: str, download_path: str | None = None)

Downloads and unzips a Kaggle dataset.

  • dataset_ref: Kaggle dataset reference in owner/dataset-slug format, for example kaggle/titanic.

  • download_path: Optional local output path. If omitted, files are saved to ./datasets/<dataset_slug>/.

Prompts

generate_eda_notebook(dataset_ref: str)

Creates a prompt for generating basic Python EDA code for the provided Kaggle dataset reference. The prompt asks for data loading, missing-value checks, visualizations, and summary statistics.

Requirements

  • Python 3.10+

  • Kaggle account and API token

  • An MCP-compatible client

Kaggle Credentials

Create a Kaggle API token from your Kaggle account settings:

  1. Go to https://www.kaggle.com/settings.

  2. Select Create New API Token.

  3. Download kaggle.json.

Use either environment variables or the standard Kaggle config file.

Option 1: Environment variables

Create a .env file in the project root:

KAGGLE_USERNAME=your_kaggle_username
KAGGLE_KEY=your_kaggle_api_key

Option 2: kaggle.json

Place kaggle.json in the standard Kaggle location:

  • macOS/Linux: ~/.kaggle/kaggle.json

  • Windows: C:\Users\<Your User Name>\.kaggle\kaggle.json

On macOS/Linux, make sure the file is not world-readable:

chmod 600 ~/.kaggle/kaggle.json

Installation

git clone <repository-url>
cd kaggle-mcp

Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

Install dependencies with one of the following methods.

Using uv

uv sync

Using pip

pip install -r requirements.txt

Running Locally

With uv:

uv run kaggle-mcp

Or run the server module directly:

python src/server.py

The server communicates over MCP stdio and is intended to be launched by an MCP client.

Claude Desktop Configuration

Open Claude Desktop settings, then go to Developer > Edit Config and add this server to claude_desktop_config.json.

If installed in the project environment:

{
  "mcpServers": {
    "kaggle-mcp": {
      "command": "uv",
      "args": ["run", "kaggle-mcp"],
      "cwd": "/absolute/path/to/kaggle-mcp",
      "env": {
        "KAGGLE_USERNAME": "your_kaggle_username",
        "KAGGLE_KEY": "your_kaggle_api_key"
      }
    }
  }
}

If using kaggle.json, you can omit the env block.

Docker

Build the image:

docker build -t kaggle-mcp .

Run with credentials from .env:

docker run --rm -i --env-file .env kaggle-mcp

Smithery

This repository includes smithery.yaml. Smithery starts the server over stdio and passes these configuration values as environment variables:

  • kaggleUsername -> KAGGLE_USERNAME

  • kaggleKey -> KAGGLE_KEY

Example Workflow

  1. Ask your MCP client: "Search Kaggle for heart disease datasets."

  2. The client calls search_kaggle_datasets.

  3. Choose a dataset reference from the results, for example user/heart-disease-dataset.

  4. Ask: "Download user/heart-disease-dataset."

  5. Ask: "Generate an EDA notebook prompt for user/heart-disease-dataset."

Project Structure

.
├── Dockerfile
├── README.md
├── pyproject.toml
├── requirements.txt
├── smithery.yaml
├── src/
│   ├── __init__.py
│   └── server.py
└── uv.lock

Downloaded datasets are saved under datasets/ by default. This directory is created at runtime when downloads are requested.

Available Tools

2 tools
download_kaggle_datasetC

Downloads files for a specific Kaggle dataset. Args: dataset_ref: The reference of the dataset (e.g., 'username/dataset-slug'). download_path: Optional. The path to download the files to. Defaults to '/datasets/'.

ParametersJSON Schema
NameRequiredDescriptionDefault
dataset_refYes
download_pathNo

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the action but lacks critical details: whether authentication is required (Kaggle typically needs API credentials), what happens if files already exist at the path, error handling, or any rate limits. The description is minimal beyond the basic operation.

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 efficiently structured with a clear purpose statement followed by parameter explanations. It avoids unnecessary fluff, though the formatting with 'Args:' could be more integrated. Every sentence adds value, making it appropriately concise.

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 complexity of downloading datasets (which often involves authentication, file management, and error cases), no annotations, and no output schema, the description is insufficient. It misses key contextual details like authentication requirements, response format, or handling of large downloads, leaving significant gaps for an agent.

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 description adds meaningful context for both parameters: it explains the format of 'dataset_ref' with an example and clarifies the default behavior and path structure for 'download_path'. With 0% schema description coverage, this compensates somewhat, but it doesn't fully detail constraints (e.g., path validity, dataset accessibility).

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

Purpose4/5

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

The description clearly states the action ('Downloads files') and resource ('for a specific Kaggle dataset'), making the purpose immediately understandable. It distinguishes from the sibling tool 'search_kaggle_datasets' by focusing on downloading rather than searching, though it doesn't explicitly contrast them.

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?

No guidance is provided on when to use this tool versus alternatives. While it's implied this is for downloading after a dataset is identified (versus searching with the sibling tool), there's no explicit mention of prerequisites, dependencies, or when-not-to-use scenarios.

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

search_kaggle_datasetsC

Searches for datasets on Kaggle matching the query using the Kaggle API.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions using the Kaggle API but doesn't disclose behavioral traits such as authentication requirements, rate limits, pagination, or what the search returns (e.g., format, fields). This leaves significant gaps for an agent to understand how to use it effectively.

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

Conciseness5/5

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

The description is a single, efficient sentence with zero waste. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.

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 lack of annotations and output schema, the description is incomplete. It doesn't cover key aspects like authentication, rate limits, return format, or error handling. For a search tool with no structured support, more context is needed to guide an agent effectively.

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?

Schema description coverage is 0%, so the description must compensate. It implies the 'query' parameter is used for searching datasets, but doesn't add meaning beyond what the schema's title ('Query') and type suggest. No details on query syntax, examples, or constraints are provided, resulting in minimal added value.

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

Purpose4/5

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

The description clearly states the action ('Searches for datasets') and target resource ('on Kaggle'), specifying it uses the Kaggle API. It distinguishes from the sibling tool 'download_kaggle_dataset' by focusing on search rather than download, though it doesn't explicitly mention this distinction.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention the sibling tool 'download_kaggle_dataset' or any other search methods, nor does it specify prerequisites like authentication or rate limits.

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

The two tools have clearly distinct purposes: one downloads a specific dataset, while the other searches for datasets. There is no overlap in functionality, making it easy for an agent to choose the correct tool for each task without confusion.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (download_kaggle_dataset and search_kaggle_datasets), using snake_case and clear action verbs. This consistency makes the tool set predictable and easy to understand at a glance.

Tool Count2/5

With only two tools, the server feels thin for a Kaggle integration, lacking essential operations like listing datasets, uploading data, or managing competitions. While the tools are functional, the scope is incomplete for typical Kaggle workflows, making the count too low for the domain.

Completeness2/5

The tool set is severely incomplete for a Kaggle MCP server. It covers downloading and searching datasets but misses critical operations such as uploading datasets, accessing competition data, or interacting with notebooks. This creates significant gaps that will hinder agents from performing common Kaggle tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

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