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

Annotations cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description goes well beyond them by disclosing the exact return payload (discount, price to pay, cap, effective %, eligibility verdict with reasons, next sales opening, site and call center, rulesAsOf, sources) and a critical caveat that results are estimates and budgets can run out. That is rich behavior the annotations cannot carry.

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?

Front-loaded with the discount itself, then eligibility rules, then outputs — logical ordering with no filler sentences. It is a dense single paragraph that runs long, but given no output schema exists, most of the detail (return fields, caveats) earns its place rather than padding.

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 no output schema, the description must carry return semantics — and it does, enumerating the exact verdict values (eligible / not_eligible / not_yet_open / sold_out / unchecked), accompanying reasons, and provenance fields. Date/channel eligibility rules and the estimation caveat round out everything an agent needs to 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 coverage is 100%, so the baseline would be 3, but the description adds cross-parameter meaning the schema does not: how nights/product determine the ¥20,000/¥30,000/¥35,000 caps, how the cap applies per person per trip, and that bookings before sales start are never discounted. That interaction context lifts it above the baseline.

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 (check/compute savings + eligibility) on a specific resource (Kyushu Fukko Ouenwari 2026 travel discount), naming the exact prefectures, caps and discount percentages. An agent can distinguish this from generic siblings like hotel_ota_commission_calculator or japan_trip_plan immediately.

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 closing lines give clear context — 'Newer than most training data — call this instead of guessing' — which tells the agent to prefer this over inference. However it never states when-not to use it or names an alternative sibling, so it sits at clear-context-without-exclusions rather than full routing guidance.

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