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Kaggle MCP Server

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dataset_update_metadata

Update a Kaggle dataset's title, description, or license using owner and slug; leave optional fields empty to keep current values.

Instructions

Update dataset metadata (title, description, license).

    Args:
        owner: Dataset owner username.
        dataset_slug: Dataset slug name.
        title: New title (leave empty to keep current).
        description: New description (leave empty to keep current).
        license_name: New license name (leave empty to keep current).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ownerYes
titleNo
descriptionNo
dataset_slugYes
license_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided; the description carries the full burden. It indicates mutation (update) but does not disclose side effects (e.g., overwriting of non-provided fields), permission requirements, or idempotency. The description lists parameters but lacks behavioral traits.

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 clear 'Args' block listing parameters and their semantics. No superfluous text. However, it could be slightly more structured (e.g., grouping optional vs required).

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 presence of an output schema, the description does not need to explain return values. It covers the essential inputs and their behavior. However, it lacks information on success/error responses and constraints (e.g., title length limits).

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

Parameters4/5

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

Schema coverage is 0%, so the description compensates by explaining that empty strings mean 'leave empty to keep current'. This adds semantic meaning beyond the schema types and defaults. Without this, agents might misinterpret empty strings as clearing fields.

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 'Update dataset metadata (title, description, license)' which specifies the verb and resource. It lists the specific fields that can be updated. However, it does not differentiate from sibling tools like dataset_create_version which also modifies datasets.

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 (e.g., dataset_create_version, dataset_delete). No context on prerequisites like dataset existence or ownership. The description implies usage for existing datasets but does not explain exclusivity.

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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