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

The annotations are all-false and uninformative, so the description carries the behavioral burden and does so well: it discloses the two verification modes (deterministic standard vs. strict with independent model review) and the unusual cost behavior ('Counts 3× toward the monthly character quota'). It also exposes the tool's checking philosophy (exact match on medical data, no added numbers). Nothing contradicts the annotations; the quota consumption even explains why readOnlyHint=false is plausible for a checker.

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?

Each sentence carries a distinct payload: checking criteria, mode selection, return shape, and quota cost — correctly front-loaded with purpose. It is dense rather than short, but the length is justified by the clinical-safety stakes and the 7-parameter surface.

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?

Given the rich 100%-coverage schema, an existing output schema, and meaningful behavioral disclosure, very little is missing. Minor absences are the interaction between date_order and the date-ambiguity rule and the precise definition of 'code', neither of which blocks correct invocation.

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 every parameter is already documented with type, default, and examples; the baseline is 3. The description reinforces the verification contract (exact numbers, unambiguous dates, protected terms) but adds no per-parameter syntax or format details beyond what the schema already provides.

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 opens with a specific verb+resource ('Check Hebrew that was produced some other way ... against its English clinical source') and enumerates the exact fidelity checks: drugs, doses, lab values, codes, numbers, date ambiguity, negation, and laterality. The phrase 'produced some other way (for example by your own model)' cleanly separates verification from the translation-producing siblings like clinical_translate and translate_batch.

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 establishes clear context — verify translations that were produced by a non-clinical_verify route ('by your own model') — which tells an agent this is the post-translation QA step rather than a generation tool. It doesn't name sibling tools explicitly or give when-not-to-use conditions, but the purpose sentence makes the intended use unambiguous.

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