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schedule_service_appointment

Captures a service appointment request for an automotive dealership. Records the customer's contact info, vehicle details, requested services, and preferred timing as a service lead. The dealership's service advisor will follow up to confirm. Requires at least one contact method (email or phone). Only available for automotive clients.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNo
vehicleYesVehicle being serviced.
customerYesCustomer contact information.
servicesYesList of requested services.
client_slugYesUnique slug identifying the dealership.
preferred_dateNoPreferred date (YYYY-MM-DD).
preferred_time_windowNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

There are no annotations, so the description carries the full burden. It discloses that the request is captured as a service lead and that the advisor will follow up, which is non-obvious from the tool name. It also states a required contact method. However, it does not mention response format or side effects like notifications.

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?

The description is three sentences long, front-loaded with the primary action, and every sentence adds critical information (lead nature, follow-up, contact requirement, client scope). No fluff or redundancy.

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?

Given the tool has nested objects and no output schema or annotations, the description covers the main purpose, the lead nature, follow-up process, and key constraints. It could mention the expected return value or error conditions, but the provided info is sufficient for an agent to invoke it correctly.

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 71%, and the description adds a crucial cross-field constraint (at least one contact method: email or phone) not enforced by the schema. It also summarizes the purpose of the parameters (customer info, vehicle details, services, timing) in context, adding value beyond the schema descriptions.

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 clearly states the tool 'Captures a service appointment request' with a specific verb and resource, and distinguishes it from siblings by emphasizing it creates a service lead rather than a confirmed appointment. The scope ('automotive dealership', 'automotive clients') further differentiates it from other tools.

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

Usage Guidelines3/5

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

The description provides context (service lead, follow-up needed) and constraints (requires email or phone, only automotive clients), but does not explicitly state when to use this tool versus alternatives like get_availability. The usage is implied rather than directly instructed.

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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