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

Annotations declare readOnlyHint=false and openWorldHint=true; the description reinforces and enriches this by disclosing that the work is sent to 'a panel of reviewers running on another company's AI model' — material third-party data egress an agent cannot infer from the annotations. It does not cover cost, latency, or retention of submitted text, which are the remaining relevant traits for an external submission.

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?

Front-loaded with the purpose in the first clause, then trypophobia-free scoping and exclusions. The mid-length 'Covers requests such as' list is justified because it broadens retrieval matching, though the description runs three dense paragraphs and could have trimmed one example cluster.

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 single-required-param, nested-object tool with no output schema, the description fully carries the load: it explains the input's completeness requirement, the external reviewer panel, the shape of the returned verdict, and the boundary cases. Nothing an agent needs to decide or invoke correctly is missing.

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 baseline is 3, but the description adds real semantics: it explains that 'the review covers the complete text submitted' and that an 'unabridged copy of the work gives a complete result', which directly steers how to populate responseUnderReview. It also implies the brief is synthesized from conversation context, matching the nested reviewBrief object.

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+resource ('independent review of AI-generated work') and enumerates the concrete artifact types it accepts (answer, code, PR, plan, analysis, draft). The second paragraph goes further and names the actual return payload (verdict, inaccuracies, gaps, follow-up questions, report card), so an agent knows exactly what it gets back.

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?

Provides both an inclusion list ('is this right', hallucination check, fact-check, adversarial review, gate review, sign-off before merging) and an explicit 'Not for' exclusion block covering claim verification, document Q&A, AI-detection, agent evaluation, and post-hoc discussion of a returned review. It also clarifies re-use ('a revised draft can be reviewed again'), which removes a common ambiguity about whether re-submission is allowed.

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