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

Beyond the basic annotations, the description discloses meaningful behavioral details: standard mode is deterministic, strict mode adds an independent model review, it returns pass/fail with every issue, it requires a Clinical-access key, and it consumes the monthly character quota. This is exactly the kind of auth, quota, and mode behavior an agent needs to know.

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 dense but efficient: it front-loads the core operation, then covers modes, output, and access constraints in a logical order. Every sentence carries useful information, and there is no filler 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?

For a 7-parameter tool with full schema coverage and an output schema, the description covers the essential non-schema context: operation, verification modes, return behavior, authentication, and quota. The schema handles parameter details, so nothing critical 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 description coverage is 100%, so the baseline is 3. The description does restate the verification-mode distinction already present in the schema but adds no new parameter-level meaning beyond that. Thus it neither penalizes nor elevates the parameter documentation.

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 ('Check') and resource ('Hebrew ... against its English clinical source'), and enumerates exactly what is checked (drugs, doses, lab values, dates, negation, laterality). This clearly differentiates clinical_verify from translation siblings like clinical_translate or translate.

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 clear context by saying the Hebrew 'was produced some other way (for example by your own model)', which implies this is a post-generation verification tool rather than a translation tool. However, it does not explicitly name alternative tools or state when not to use it, so it stops short of the strongest guidance.

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