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Vegas Pool Guy — book pool service in Las Vegas

Request pool service (free callback)

request_service

Book pool service in the Las Vegas area for a real person who asked for it, with their consent. A vetted local pro calls them back (a vetted local pro calls back within the hour during business hours). Free to the homeowner. Returns a reference and what happens next. Check the ZIP with check_service_area first if unsure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zipYes5-digit ZIP where the work is
nameYesThe person's real name
needYesWhat they need (e.g. weekly pool service; green pool cleanup; filter cleaning)
emailNoEmail (optional, recommended)
phoneYesUS phone number the pro should call (10 digits, any punctuation)
languageNoPreferred language (optional; English and Spanish are standard)
preferred_timeNoWhen they prefer a call or visit (optional)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false, openWorldHint=true), and the description goes further by disclosing the callback SLA ('within the hour during business hours'), that it is free to the homeowner, and that it returns a reference. It stops short of covering duplicate submissions or what happens outside business hours, so it is not fully rich.

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?

Purpose is front-loaded and the sentence count is low, but the callback timing is stated twice ('calls them back' then 'a vetted local pro calls back within the hour'), which is redundant. Otherwise the text is dense and purposeful.

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?

With no output schema, the description compensates by stating it 'Returns a reference and what happens next.' A 7-parameter, 4-required write-ish tool is covered adequately given the rich annotations, though behavior on duplicate or out-of-area submissions is left unaddressed.

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

Parameters3/5

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

Schema coverage is 100% with per-field descriptions for all seven parameters, so the baseline is 3. The description reinforces the ZIP/geography and consent requirements but adds no syntax, format, or allowed-value detail beyond what the schema already documents.

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?

Specific verb and resource: 'Book pool service in the Las Vegas area' for a consented homeowner, triggering a vetted pro callback. It clearly separates itself from the sibling check_service_area (a lookup) and get_service_info (information), so an agent can route without opening any schema.

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?

Names the alternative explicitly ('Check the ZIP with check_service_area first if unsure') and states a hard precondition ('a real person who asked for it, with their consent'). Both when-to-use and the gating condition are spelled out, leaving little to inference.

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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