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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds useful context beyond that: the response is a 'live departures board card' and the covered data includes fees and a 1–5 seasickness meter scoped by month and morning/afternoon boat. It does not discuss how 'live' data is refreshed or how stale the board may be, which is the main remaining gap.

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 purpose and covered data are front-loaded in the first sentence, followed by the output hint and usage examples. The bilingual example pair adds genuine value for query matching, though the sentence is long and dense with domain detail that could be slightly tightened.

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 formatting needn't be explained, and the description still gives a one-line hint about the departures-board card. With full schema coverage, all-optional parameters, and clear annotations, 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 coverage is 100% and all four parameters are documented with defaults and enums, so the baseline is 3. The description adds interpretive value by explaining that month drives the monthly roughness assessment and that boat_time selects the morning vs. afternoon seasickness reading, tying the two parameters to a concrete user-facing purpose.

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 resource (inter-island public speedboat ferries in the Galápagos) and enumerates exactly what data it covers: departure times among four named islands, crossing time, fares, water taxi/dock fees, and monthly roughness. This is clearly separable from siblings like live_conditions, estimate_trip_cost, or month_guide.

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 opens with an explicit use case ('Use this for inter-island ferry questions') and supplies two concrete example queries, one in Spanish, which pins down the intended trigger domain. It does not explicitly name alternatives or exclusions (e.g., when to prefer month_guide for general seasonal advice), so it stops 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.