colab_job_cancel

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 the termination mechanism (SIGTERM to the process group), which is genuinely useful for reasoning about graceful vs. forced shutdown. However, it omits idempotency, error behavior for unknown/finished jobs, and whether the call blocks until exit.

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?

A single front-loaded sentence with the operation and its mechanism; no filler or redundancy.

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 no explanation, and the mechanism detail helps. Still missing are the failure/edge-case contract (nonexistent or already-finished job), idempotency, and the meaning of the session parameter for a mutation tool with zero annotation coverage.

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?

Schema description coverage is 0% and the description does not mention either parameter. The optional 'session' argument is entirely unexplained, and there is no hint about job_id format or where to source it (colab_job_start/colab_job_list).

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?

States a specific verb (terminate) and a precisely scoped resource (a running background job), which distinguishes it from the session-scoped colab_stop and from the read-only colab_job_status/colab_job_logs siblings.

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?

No when-to-use guidance, no prerequisites, and no mention of alternatives such as checking colab_job_status first or using colab_job_list to obtain a job_id. The agent must infer the workflow entirely.

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