kaggle-mcp

kaggle_eda_dataset

Download a dataset and return a compact exploratory summary—shape, dtypes, missingness, target distribution, and top numeric correlations—computed locally with pandas.

Instructions

Download a dataset and return a COMPACT exploratory summary — shape, dtypes, missingness, target distribution, and top numeric correlations — computed locally with pandas. Never streams raw rows into context. This is the 'find data -> understand it' primitive that most Kaggle MCP servers lack (they dump raw files or just emit a prompt). dataset is 'owner/slug'.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset": "<dataset>"
    },
    "name": "kaggle_eda_dataset"
  }
}

Input Schema

Name	Required	Description	Default
`file`	No
`target`	No
`dataset`	Yes
`max_files`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.4.2Jul 30, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already provide readOnlyHint=false and destructiveHint=false. The description adds valuable behavioral context: the summary is computed locally with pandas and raw rows are not streamed. This clarifies the scope and side-effect-free nature beyond what annotations offer.

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 concise with three sentences, each adding value. It front-loads the main action and output, then adds a key behavioral note and contextual remark. No extraneous information.

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

Completeness4/5

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

Given the tool has 4 parameters (with no schema descriptions) but an output schema exists, the description adequately covers the output nature. However, it lacks details on error handling, prerequisites (e.g., authentication), or how the parameters interact. Slight gaps in completeness.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must explain parameters. It only explains the 'dataset' parameter as 'owner/slug'. The other three parameters (file, target, max_files) are not described, leaving their meaning and usage unclear. The description could have compensated by linking target to the mentioned 'target distribution' or file to multi-file datasets.

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 tool downloads a dataset and returns a compact exploratory summary with specific elements (shape, dtypes, missingness, target distribution, top numeric correlations). It distinguishes itself from sibling tools by noting that most Kaggle MCP servers lack this primitive, making the purpose unambiguous.

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

Usage Guidelines4/5

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

The description implies this tool is for initial data understanding ('find data -> understand it') and explicitly states it never streams raw rows. However, it does not explicitly mention when to use this over alternatives like kaggle_get_dataset_metadata or kaggle_dataset_preview, leaving some ambiguity.

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

Deploy Server