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Kaggle MCP Server

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Python
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dataset_create_version

Create a new version of an existing Kaggle dataset by supplying owner, dataset slug, version notes, and optional file tokens.

Instructions

Create a new version of an existing dataset.

    Args:
        owner: Dataset owner username.
        dataset_slug: Dataset slug name.
        version_notes: Notes describing this version.
        file_tokens: Comma-separated file tokens from file_upload.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ownerYes
file_tokensNo
dataset_slugYes
version_notesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It only states the action and lists parameters, with no disclosure of behavioral traits such as auth requirements, rate limits, or consequences of creating a version (e.g., immutability).

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 clear sentence followed by a structured parameter list, with no unnecessary information. All content earns its place.

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 the existence of an output schema and moderate parameter count (4), the description covers the basic purpose and parameters but lacks information about return values or side effects, making it adequate but not fully complete.

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 coverage is 0%, but the description adds brief explanations for each parameter (e.g., 'Dataset owner username', 'Comma-separated file tokens from file_upload'), providing meaning beyond the schema's type-only definitions. However, the descriptions are minimal and could be more detailed.

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 'Create a new version of an existing dataset' uses a specific verb 'create' and resource 'version of a dataset', clearly distinguishing it from siblings like 'dataset_create' (create new dataset) and 'dataset_update_metadata' (update metadata).

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 by stating the action and listing required parameters, but it does not provide explicit guidance on when to use this tool versus alternatives or any prerequisites (e.g., existing dataset).

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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