Kaggle MCP Server

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kernel_push

Push or save a notebook to Kaggle by providing its title and source code, with optional language, kernel type, and privacy settings.

Instructions

Push/save a notebook to Kaggle.

    Args:
        title: Notebook title.
        text: Notebook source code.
        language: Language (python, r).
        kernel_type: Type (notebook, script).
        is_private: Whether notebook is private.

Input Schema

TableJSON Schema

Name	Required	Default
`text`	Yes
`title`	Yes
`language`	No	python
`is_private`	No
`kernel_type`	No	notebook

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

C2.9/5.0

Behavior2/5

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

No annotations are provided, and the description focuses only on parameter listing without disclosing behavioral traits like error handling, authentication requirements, or side effects (e.g., overwriting existing kernels). The tool's side effects (pushing a notebook) are implied but not detailed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short but includes a unnecessary docstring format. It could be more front-loaded, and the bullet list is clear but not dense.

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 5 parameters (2 required), no schema descriptions, and no annotations, the description is incomplete. It does not explain return values (though an output schema exists) or prerequisites like Kaggle API authentication.

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%, so the tool description must compensate. However, each parameter is merely restated (e.g., 'title: Notebook title.') without additional constraints, formats, or examples. This adds minimal value over 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 clearly states 'Push/save a notebook to Kaggle', which is a specific verb+resource combination. It distinguishes the tool from siblings like kernel_pull and kernel_output, which handle retrieval and output.

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 is provided on when to use this tool versus alternatives such as kernel_pull or kernel_output. The description does not mention when not to use it or any prerequisites.

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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