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

Beyond the read-only annotations, it explains that generation is asynchronous and may take minutes, why worker-backed deployments return a job_id, and that seat jobs must not be queued in parallel. These are material operational details for correct invocation and polling.

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 four tight sentences with no filler. It front-loads the core purpose, then layers in polling behavior and the concurrency constraint in a logical order.

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 read-only status-checking tool with no output schema, it is complete enough to call correctly. It covers the upstream producer, polling expectations, terminal states, and the parallel-execution restriction.

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 coverage is 100%, so the schema already documents job_id and the required context parameter. The description only adds the origin of job_id via generate_code_for_test_case, which is useful but does not substantially extend parameter semantics.

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?

It states a specific verb and resource: checking a background code-generation job. It explicitly ties the tool to generate_code_for_test_case, so an agent can distinguish it from unrelated status-checking siblings such as check_ai_review_status or check_ai_crawl_status.

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 gives explicit triggering context: use after generate_code_for_test_case. It also specifies polling cadence (about every 30s), terminal conditions ('completed', 'failed', or 'timed_out'), and a concurrency caveat for seat jobs.

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