Get design review

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

The description adds substantial behavioral context beyond the annotations. It explains 'Result reads do not consume review units', which is safety-relevant. It also gives actionable guidance on how to interpret two result fields: 'Act on review.measurements.violations unconditionally' and 'Act on review.findings only when provenance.model_backed is true and coverage.state is full or partial'. These nuances are not present in annotations and are crucial for correct use. There is no contradiction with annotations.

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 compact and front-loaded. It states purpose in the first sentence, then provides specific usage (polling) and a second critical behavioral rule (result units) followed by a third interpretation rule. Every sentence earns its place with no fluff or repetition.

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?

The description is highly complete for a read-only view tool. It tells the agent about the output views, the impact of read operations on review units, the polling mechanism, and the provenance/coverage gate for findings. Given that the output schema exists and annotations already convey idempotence and safety, nothing essential is missing for correct use.

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 schema already describes 'view' and its enum/default, covering 50% of parameters. The description enhances meaning by connecting the view options to 'compact, focused, or evidence' and specifying that the job_id refers to an 'existing review job'. While it does not provide detailed semantic descriptions for each enum value, it goes beyond the schema's minimal value descriptions and reinforces the job_id context.

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 states a specific verb ('Get'), a clear resource ('design review'), and the exact variants of views available (status, compact, focused, evidence). The phrase 'existing review job' immediately distinguishes it from sibling tools like design_review or design_recheck, which likely create or modify jobs. An agent can reliably identify this as the read/query tool among the sibling set.

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 clearly tells the agent when to use it: to get status or views of an existing review job, and it gives a concrete operational guideline ('Poll no fetched than the returned poll_after_ms'). It does not explicitly state when not to use it or mention alternatives (e.g., 'use design_review to start a new review'), but the context of 'existing review job' implies it is not for creating or canceling. The guidance is clear but weak on exclusion.

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