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Honee – cruise · honeefy

Advisor's opinion on one offer

advise
Read-onlyIdempotent

Asks the provider's cruise advisor for a short, honest opinion on ONE offer from find_cruises: overall fit, or which cabin category or fare suits the travellers. Use it ONLY when the user asks about ONE specific offer ('is this right for me', 'which cabin', 'which fare') -- not after every find_cruises and never for a whole list. Quote the opinion as the advisor's. It does NOT search, book or compare several offers; it is limited to a few calls per consultation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
focusNoWhat the opinion should focus on.
handleNoConsultation handle returned by a previous call (structuredContent.handle). Pass it on every call so wishes are remembered; omit it on the very first call.
languageNoLanguage of the answer. Defaults to the provider's language.
cruise_idYescruise_id of an offer returned by find_cruises.
travellersNoOptional short description of the travel party, e.g. 'couple, first cruise'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent and non-destructive, so the safety profile is covered; the description adds genuinely new behavior: a per-consultation call budget ('limited to a few calls') and the requirement to attribute the output as the advisor's. It stops short of describing how the opinion is returned or how the handle drives follow-up state, which keeps it below a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Every sentence carries distinct load: purpose, trigger, exclusion, attribution rule, call budget. The scope constraint ('ONE offer') is front-loaded and there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, and the description covers what matters most: what the tool returns conceptually, that it must be quoted as the advisor's, and its call-frequency limit. It omits how the returned handle should be threaded into subsequent calls, a small gap against a five-parameter tool with stateful follow-up.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning: cruise_id must be an offer produced by find_cruises, focus is implicitly enumerated via 'overall fit, or which cabin category or fare', and the mention of a 'consultation' contextualises the handle parameter's continuity role.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('asks the provider's cruise advisor for a short, honest opinion on ONE offer from find_cruises') and explicitly bounds the subject to a single offer. It is immediately distinguishable from find_cruises (search) and other profile siblings by naming what it is not: a searcher, booker, or comparator.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use triggers ('is this right for me', 'which cabin', 'which fare') and equally explicit exclusions ('not after every find_cruises', 'never for a whole list'). The agent needs no inference to decide between this and find_cruises.

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

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