Use when an existing customer wants to verify identity (OTP or magic link) before assistant-led booking.
| Name | Required | Description | Default |
|---|---|---|---|
| identifier | Yes | ||
| method_preference | No | auto | |
| channel_preference | No | auto |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavioral traits itself. It states the tool verifies identity but does not disclose whether it sends messages, creates tokens, or has side effects. The response format and next steps are also omitted, leaving the agent uncertain about the outcome of calling this tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that immediately states when to use it. No wasted words, and it reads naturally.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three parameters, no output schema, and no annotations, the description is too minimal. It provides the triggering context but not the tool's behavior, return value, or how it relates to the subsequent verification steps (e.g., verify_customer_otp). The agent lacks critical information to complete the flow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides no descriptions (0% coverage), so the description must explain the parameters. It only hints that OTP and magic link are relevant to method_preference, but does not explain identifier, the meaning of 'auto' defaults, or channel_preference. This is a significant gap for an agent to construct a valid call.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as starting customer identity verification, with a specific use case (existing customer, before assistant-led booking). The verb 'use when' plus the resource make the purpose unambiguous, and it distinguishes from sibling verification tools by implying this is the initiation step.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states the condition under which to use the tool: when an existing customer wants to verify identity via OTP or magic link before booking. It does not list alternative tools or exclusions, but the 'before assistant-led booking' context provides clear situational guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Some tools overlap in purpose, such as check_availability vs get_customer_availability and the various verification entry points. Descriptions clarify the context of each, but an agent could still misselect without careful reading.
All tools follow a consistent verb_noun pattern with snake_case, e.g., check_availability, create_booking_request, get_booking_status. This makes the naming predictable and easy to navigate.
With 20 tools, the server is on the heavier side but the scope is broad, covering booking, customer verification, payments, and provider matching. The count feels slightly high but each tool serves a distinct step in the workflow.
The tool surface covers the full booking lifecycle from availability and quote to payment and final creation. Notable gaps include lack of update or cancel booking operations, but the core flow is well covered.