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push_notebook

Upload a notebook to Kaggle and run it on free GPU or TPU resources, solving the need for powerful hardware without local setup.

Instructions

Upload notebook and run on free GPU/TPU

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gpuNo
tpuNo
nameNo
pathYes
privateNo
internetNo
dataset_sourcesNo
timeout_secondsNo
competition_sourcesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

C2.8/5.0
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 disclosure burden. It reveals the core upload-and-run behavior but not critical operational traits: that this likely submits an asynchronous remote execution, that results require polling via kernel_status/kernel_output, that 'free' hardware implies quota limits, or what error/return behavior to expect. For a tool that triggers remote compute, this is a substantial gap.

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?

A single efficient fragment, front-loaded with the actionable verb. Zero wasted words. It is on the terse side for a tool with 9 undocumented parameters, but the under-specification is a completeness problem, not a conciseness problem — the sentence itself earns its place.

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

Completeness1/5

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

Given 9 parameters with 0% schema coverage, no annotations, no output schema, and a 70+ sibling toolset that includes pull_notebook, init_kernel, update_kernel, preview_notebook, and kernel_status, this description leaves nearly everything an agent needs unanswered: parameter semantics, the kernel lifecycle, how results are retrieved, and how to choose among sibling tools. Severely incomplete.

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% — all nine parameter descriptions are empty strings, so the description must compensate. It weakly maps path (notebook to upload) and gpu/tpu (hardware choice) via the phrase 'run on free GPU/TPU', but six parameters (name, private, internet, dataset_sources, competition_sources, timeout_seconds) receive zero explanation anywhere.

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 states a specific verb and resource ('Upload notebook') plus the execution context ('run on free GPU/TPU'), which distinguishes it from pull_notebook by transfer direction. It earns a 4 rather than 5 because it doesn't explicitly differentiate from other notebook/kernel siblings like init_kernel, update_kernel, or preview_notebook.

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 'run on free GPU/TPU' phrase implies the use case: push a notebook for cloud execution. However, there is no explicit when-to-use guidance, no exclusions, and no mention of the natural alternatives (pull_notebook for retrieving, kernel_status/kernel_output for inspecting the run). With roughly twenty notebook-related siblings, the routing guidance is only implied, not stated.

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