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Kaggle MCP Server

by art-qalam-fr
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Python
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kernels_list

Search and list Kaggle notebooks by keyword, competition, or dataset. Filter and sort results by hotness, votes, date, and more.

Instructions

Search and list Kaggle notebooks/kernels.

    Args:
        search: Search term.
        competition: Filter by competition.
        dataset: Filter by dataset.
        sort_by: Sort order (hotness, commentCount, dateCreated, dateRun, relevance, voteCount).
        page: Page number.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
searchNo
datasetNo
sort_byNo
competitionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.1/5.0
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 search/list but does not disclose behavioral traits like idempotency, rate limits, or whether it's read-only. The description is minimal and insufficient for a tool with no annotations.

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 sentence and an Args list. It is front-loaded with the main action. No extraneous text.

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

Completeness3/5

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

Given 5 parameters (all optional) and an output schema present, the description covers the basic search/filter capability but lacks usage context. It is minimally complete for a listing tool.

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%, yet the description merely repeats parameter names with defaults. For 'sort_by', it lists example values but not as enums. The description adds little meaning beyond what the schema field names imply.

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 notebooks/kernels' with specific verb and resource. The name 'kernels_list' combined with the description distinguishes it from sibling tools like 'competitions_list' or 'datasets_list'.

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 on when to use this tool versus alternatives. With many sibling tools, explicit when-to-use or when-not-to-use is missing, leaving the agent to infer context.

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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