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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. Beyond that, the description discloses the result shape that matters for decision-making — per-person vs whole-aircraft seat selling, JPY 'from' pricing, departure heliport, and whether OlaChill confirms the slot with the operator — which is genuine behavioral context rather than restatement.

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?

It is front-loaded with the trigger sentence, and the exclusion sentence is last, which is the right ordering for an agent skimming. The middle enumeration of use cases is long and somewhat repetitive, and the return-field list partly duplicates the output schema, but nothing is off-topic.

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 read-only discovery tool with a full output schema and 100% parameter coverage, the description supplies the decision-relevant context: trigger, scope, exclusions, and the differentiating result fields. An agent has everything needed to choose this tool over its twelve siblings and call it 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 100%, so the schema already documents all six parameters, and the enum semantics are spelled out in the schema itself. The description does help map natural-language intent onto enum values (Mt Fuji/Yokohama/Osaka/Kyoto → area, night-view → time_of_day, charter/heli-taxi → service_type), but that is inferable from the enums. Baseline 3 is appropriate.

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+resource (search helicopter experiences/flights in Japan) and then enumerates concrete subtypes — sightseeing loops, night-view, charter, heli-taxi transfer — which pins down scope precisely. It explicitly distinguishes itself from search_travel_products for non-helicopter tours and from airline/ground transport, so an agent can separate it from every sibling without opening a schema.

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 an explicit trigger ('use this when the user wants a helicopter experience in Japan, whether or not they mention OlaChill'), names the fallback sibling for non-helicopter tours, and lists clear exclusions (airline flights, ground transport). Both the when and the when-not conditions are stated rather than implied.

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