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Check a booking request

booking_status
Read-onlyIdempotent

Where a request_booking request stands. A home cleaning reference (hmr_): waiting for the customer on WhatsApp, declined, expired, awaiting payment (with the payment link), finding a cleaning expert, booked (with the next visit), done or cancelled; for changes, the customer messages Helen on WhatsApp. A call-back reference (hmc_): booked with its window, called, or closed; to move it, call request_booking again with a new window. Never includes the customer's details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
referenceYesThe reference request_booking returned (starts with hmr_ or hmc_).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / reference / description
      Previous value: -"The reference request_booking returned (starts with hmr_)."New value: +"The reference request_booking returned (starts with hmr_ or hmc_)."
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the bar is lower, and the description clears it by disclosing the full set of possible return states and the privacy boundary (never includes the customer's details). It adds no auth or rate-limit context, but for a read tool with no output schema the state enumeration is the meaningful disclosure.

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?

It is a single front-loaded sentence with no filler, and each semicolon clause carries distinct status information. The style is dense and somewhat run-on, but nothing is redundant and the purpose comes first.

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?

With no output schema, the description carries the return values by enumerating the possible statuses, and annotations already supply the safety profile. The one parameter is fully documented, so an agent has everything needed to call and interpret this tool.

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 goes beyond the schema by explaining what the hmr_ and hmc_ prefix families actually signify behaviorally. That interpretation adds real meaning over the schema's bare 'starts with hmr_ or hmc_'.

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

Purpose4/5

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

The opening clause states a specific purpose — retrieving where a request_booking request stands — and the hmr_/hmc_ split makes the resource scope concrete. It is clearly a read-only status lookup distinct from request_booking, though it never names the sibling tools directly.

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 gives concrete routing for the two follow-up cases: changes go to Helen on WhatsApp, and an hmc_ booking is moved by calling request_booking again with a new window. It stops short of an explicit 'use this after request_booking returns a reference' statement, but the context is inferable and the alternatives are named.

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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