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

Annotations already indicate read-only, idempotent, non-destructive, closed-world behavior. The description adds significant behavioral context: the polling interval, status values to wait for, and the directive to follow retry_after_seconds and next_action. This goes well beyond the annotation-provided info and helps the agent understand expected interaction patterns.

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?

The description is structured well: first states the purpose, then gives operational instructions, and ends with a helpful example. It is slightly longer than strictly necessary but every sentence provides useful information (polling, retry handling, structured_result preference, example). No waste.

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?

With one simple parameter, an output schema present, and strong annotations, the description covers all essential aspects: what the tool does, when to use it, how to poll, and what to do in each outcome. The example clarifies the API endpoint. It is complete for an agent to correctly select and invoke the tool.

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 input schema fully describes job_id as 'The job_id returned when the task was started.' (100% coverage). The description enhances this with a concrete example URL showing how to pass job_id, and clarifies that it comes from a job-tool. This adds practical value beyond the schema.

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 the tool's function: 'Get the status or result of a job' and identifies the specific sources of jobs (deep_research, translate_pdf, make_slides). It is easily distinguished from sibling tools which are all app-centric (build_app, get_app, list_apps).

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 explicit usage guidance: poll every 15-30 seconds until status is done/error, follow retry_after_seconds and next_action while pending, and prefer structured_result when complete. This clarifies when and how to use the tool, with concrete polling and handling instructions.

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