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

'Non-mutating computation' reinforces the readOnlyHint=true annotation and the POST /suggest endpoint mapping adds useful backend context. However, it says nothing about rate limits, model dependencies, or what the suggestion payload looks like, so the added value beyond annotations is modest.

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?

Three short front-loaded fragments: purpose, safety trait, endpoint. Zero filler and everything an agent needs to triage scanning is first.

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?

With 8 parameters, nested evidence objects, and no output schema, the definition never describes the shape of the returned suggestions or how many/what form they take (max_results caps count but not structure). For a non-trivial engine call this leaves a real gap, though the schema covers most inputs.

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 88%, so the schema already documents nearly every parameter (age units, evidence shape, suggest_method enum, dev_mode header). The description adds no parameter-level meaning beyond that, which is the expected baseline when the schema does the heavy lifting.

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 ('suggest additional symptoms/observations') and the goal ('speed up the interview'), which distinguishes it from infermedica_diagnosis and infermedica_parse. It stops short of explicitly naming the sibling it competes with or the point at which suggest should be preferred.

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 phrase 'to speed up the interview' implies the usage context (mid-interview, before diagnosis), but there is no explicit when-to-use/when-not or reference to alternatives like diagnosis or rationale. Usage is inferable rather than stated.

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