Kaggle MCP Server

Overview Schema Source Related Servers Discussions Admin

model_update

Modify an existing Kaggle model's title, subtitle, description, or privacy setting. Specify owner and model slug to apply changes while keeping unspecified fields intact.

Instructions

Update an existing Kaggle model.

    Args:
        owner: Owner username.
        model_slug: Model slug to update.
        title: New title (leave empty to keep existing).
        subtitle: New subtitle (leave empty to keep existing).
        description: New description (leave empty to keep existing).
        is_private: New privacy setting (None to keep existing).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`owner`	Yes
`title`	No
`subtitle`	No
`is_private`	No
`model_slug`	Yes
`description`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Oct 1, 2026

TDQS

A3.7/5.0

Behavior3/5

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

With no annotations, the description must disclose behavioral traits. It explains update behavior and how to preserve existing values via empty strings or None. However, it omits permissions, idempotency, and response details.

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 front-loaded with the main purpose and then provides a clear bulleted list of parameters. While the Args section is somewhat lengthy, it is necessary given the lack of schema descriptions.

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?

Covers all parameters and their usage, but does not address error conditions, preconditions (e.g., model existence), or return value. The presence of an output schema reduces the need for return value explanation, so completeness is adequate but not exceptional.

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

Parameters5/5

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

Schema description coverage is 0%, so the description fully explains each parameter: owner and model_slug are required, title/subtitle/description can be left empty to keep unchanged, is_private can be None to keep existing. This adds value beyond the schema.

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 starts with 'Update an existing Kaggle model,' clearly stating the primary action and resource. This distinguishes it from sibling tools like model_create, model_delete, and model_get.

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 explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., ownership) or scenarios where other tools would be more appropriate.

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

Deploy Server