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Cancun4me — Reservas de Hotel

search_hotels

Busca tarifas AO VIVO (sempre em tempo real, nunca cache) nos hoteis do catalogo Cancun4me para as datas informadas, agrupadas por hotel (menor preco de cada um — o 'a partir de'). Sempre pelo menos 1 adulto; children_ages e a lista de idades das criancas (uma por crianca), ex.: [7, 12]. destino_ou_hotel filtra por nome (opcional). Guarde o search_id da resposta — ele e necessario em ver_quartos, get_quote e create_payment (a busca fica valida por um tempo limitado; se expirar, refaca). Datas no formato YYYY-MM-DD. Precos ja incluem o desconto do tier do hospede (cadastrado 8% / vip 12%) e o markup de venda — sao os precos finais de compra. Membro do Clube nao tem desconto imediato no preco: o beneficio e credito pos-confirmacao (20% da primeira reserva, 10% das seguintes) para a proxima reserva.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tokenYes
adultsNo
checkinYes
checkoutYes
languageNoen
children_agesNo
destino_ou_hotelNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries full burden. It discloses real-time no-cache behavior, limited-time validity of search_id, final pricing with tier discounts/markup, and Club member credit logic—far beyond what the bare schema offers.

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

Conciseness4/5

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

The description is dense and front-loaded with the core function, then adds necessary detail. Every sentence adds value, though the length is considerable; it could be slightly more structured, but the complexity justifies its thoroughness.

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

Completeness5/5

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

For a 7-parameter tool with no annotations or output schema, the description covers all essential aspects: input semantics, output hint (search_id), time validity, downstream integration, and pricing nuances. It is a model of completeness for an AI agent to select and invoke the tool correctly.

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

Parameters5/5

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

The schema has zero descriptions, but the description explicitly explains children_ages with an example, destino_ou_hotel as optional filter, minimum adult requirement, and YYYY-MM-DD date format. It fully compensates for the schema gap.

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?

The description opens with a specific verb ('Busca') and resource ('tarifas AO VIVO ... nos hoteis do catalogo Cancun4me'), clearly distinguishing this rate-search tool from siblings like list_hotels or get_quote. It also states the output grouping (by hotel, lowest price) and the search_id flow, making the purpose unambiguous.

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

Usage Guidelines4/5

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

It provides clear context: always live rates, at least one adult, save search_id for downstream tools (ver_quartos, get_quote, create_payment), and date format. Though it doesn't explicitly name alternatives or exclusions, the operational workflow is clearly implied.

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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