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Kaggle MCP Server

by art-qalam-fr
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Python
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datasets_list

Search and list Kaggle datasets by search term, sort order, file type, and page to find relevant data for analysis and machine learning projects.

Instructions

Search and list Kaggle datasets.

    Args:
        search: Search term.
        sort_by: Sort order (hottest, votes, updated, active).
        file_type: Filter by file type (csv, json, sqlite, etc).
        page: Page number.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
searchNo
sort_byNo
file_typeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.3/5.0
Behavior2/5

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

No annotations provided. The description does not disclose behavioral traits such as whether the tool is read-only, side effects, rate limits, or pagination behavior. Minimal info beyond basic purpose.

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 concise, with a single line purpose and a list of parameters. No redundant information, though it could be slightly more structured.

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 4 parameters and an output schema, the description is minimal. It does not explain the output, pagination, or provide examples. Lacks completeness for a search/list tool.

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%. The description provides brief semantics for each parameter (e.g., 'search term', 'sort order'), adding meaning beyond names but lacking allowed values or format details.

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 'Search and list Kaggle datasets.' It is a specific verb+resource combination and distinguishes from sibling tools like dataset_get or competitions_list.

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

Usage Guidelines3/5

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

The description implies usage for searching/listng datasets but does not explicitly state when to use this vs alternatives (e.g., dataset_get for a single dataset). No exclusions or context provided.

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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