l2kc_cancel_job

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

With no annotations, the description carries the full burden, and it does disclose two useful traits: the data are fixtures rather than a live account, and the official Kaggle CLI cannot cancel a running kernel. It still omits what happens on cancel (job state afterward, reversibility, permission/auth requirements, error behavior for invalid job_id).

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?

Front-loaded with the action in three words, then the caveats. Tight overall; the Kaggle-CLI sentence is borderline tangential but does justify why this tool exists, so it largely earns its place.

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?

An output schema exists, so return values need not be described. But for a mutation-style tool with no annotations and an undocumented parameter, the description leaves out consequences of cancellation and how job_id is obtained, so it is only minimally sufficient.

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 single parameter job_id has 0% schema description coverage and the description says nothing about it — no format, no indication that it comes from l2kc_submit_job or l2kc_list_jobs. The description therefore fails to compensate for the schema gap on the only parameter that matters.

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 first sentence gives a specific verb and resource ("Cancel a job"), so the operation is unambiguous. However, the sibling list contains gpu_cancel_job, and the description never explains why an agent would pick l2kc_cancel_job over it; the Kaggle CLI remark hints at a difference without stating it.

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?

"Mock data are TEST FIXTURES, not a live account" implies the tool is for exercising a test/demo environment and warns against trusting results as live, which is real usage context. But there is no explicit when-to-use, when-not-to-use, or named alternative (gpu_cancel_job), so the guidance stays implied.

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