prepare_doctor_visit

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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=false), so the bar is lower, yet the description still adds real behavioral context: only 'approved' values are surfaced (pending ones excluded), an explicit cap of 15 abnormal values, a deterministic summary, and inclusion of a disclaimer. The specialty echo-not-filter behavior is also disclosed. It does not discuss freshness or data-source limits, keeping it at 4.

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 tight clauses: what the briefing contains, when to use it, and the specialty caveat. The output contents are front-loaded and every sentence adds information with no filler.

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?

An output schema exists, so return-value documentation is not strictly required, yet the description still summarizes the payload's composition. Combined with the usage trigger and the specialty caveat, an agent has everything needed to select and invoke this tool correctly.

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 0%, so the description must carry the parameter burden, and it does: it states specialty is optional, that it is merely a label echoed in the output, and explicitly warns the content is not filtered by it. That is exactly the semantics an agent needs to avoid a wrong mental model; only the format/valid values of the label remain unspecified.

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 names a specific deliverable (a one-call doctor-visit briefing) and enumerates its exact contents: deterministic health summary, latest abnormal approved values with severity levels capped at 15, due/overdue screenings, pending-review count, and disclaimer. This is clearly distinguishable from siblings like get_health_summary, get_screening_recommendations, and list_pending_reviews because it declares itself as the aggregate of those.

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?

It gives explicit triggering contexts — 'Use before an appointment or when the user asks "what should I tell my doctor"' — which is strong when-to-use guidance. It does not name alternatives it might be confused with (e.g., get_health_summary) or state when not to use it, so it falls short of a 5.

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