kaggle_push_kernel

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

The description discloses non-trivial behavioral traits: it creates or updates and queues a run (write operation), is asynchronous, and requires polling via kaggle_kernel_status. Annotations already indicate not read-only and not destructive, which the description reinforces and expands upon.

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

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

Three sentences, each adding essential information: action, prerequisite, default setting, and async behavior. No wasted words; the description is front-loaded with the core purpose.

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

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

Given the single parameter, the presence of an output schema (not shown but noted), and the annotations covering safety, the description provides complete guidance for using the tool. It explains the workflow, input, and expected follow-up (polling).

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

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

The schema has 0% description coverage for its only parameter 'folder'. The description adds meaning by stating it comes from a local folder and mentions the required kernel-metadata.json, clarifying the expected input. This compensates for the schema gap, though more detail on folder structure could be helpful.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it pushes (creates/updates and queues-run) a notebook from a local folder using Kaggle's free GPU/TPU, a specific verb-resource combination. It distinguishes from siblings like kaggle_pull_kernel (which pulls) and kaggle_kernel_status (for polling).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use: to push and run a notebook locally. It mentions a prerequisite (kernel-metadata.json) and a default behavior (private). It does not explicitly list alternatives or when-not-to-use, but the context is sufficient for most agents.

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