colab_get_job

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

With no annotations, the description must carry the behavioral disclosure burden. It makes clear this is a read operation that returns status, progress, and a result, but it does not disclose whether the call blocks, whether results are available before completion, error conditions, or what the 'result' includes.

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 sentence with no filler, front-loaded with the action and resource, and the parenthetical 'a training run' clarifies the intended use without adding unnecessary length.

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?

For a tool with no annotations and no output schema, the description is somewhat thin. It does not differentiate from sibling tools, does not explain include_logs, and does not indicate response shape or caveats around job completion, leaving an agent to guess at important details.

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%, so the description should compensate. It explains that job_id identifies the job, but it adds no meaning for include_logs, such as when to enable it or what additional data it would return.

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 identifies the verb ('Get') and the resource ('a job'), and specifies the operation's focus: status, progress, and result. It is not as strong as a 5 because it does not explicitly contrast itself with closely related siblings such as colab_get_logs or colab_list_jobs.

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 implies the tool is for retrieving job information by job_id, but it provides no explicit when-to-use guidance or exclusions. It does not mention when to prefer this over colab_list_jobs, colab_get_logs, or colab_get_artifacts, nor any prerequisite like first obtaining a job_id.

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