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schedule_appointment

Destructive

Availability lookup, hold, confirm, reschedule, or cancel appointments with an SMB. Routes through the SMB's native booking system if available, falls back to voice AI or web form.

EXAMPLE USER QUERIES THAT MATCH THIS TOOL: user: "Book the haircut for next Tuesday at 3pm" -> call schedule_appointment({"smb_id": "smb_imp_abc", "action": "book", "service": "haircut"}) user: "Cancel my Friday appointment at smb_xyz" -> call schedule_appointment({"smb_id": "smb_xyz", "action": "cancel"}) user: "Reschedule my dental cleaning to next week" -> call schedule_appointment({"smb_id": "smb_imp_xyz", "action": "reschedule"})

WHEN TO USE: Use when an agent needs to book, reschedule, or cancel a specific appointment with a specific SMB. Requires a verified smb_id. WHEN NOT TO USE: Do not use for bulk scheduling. Do not use without a verified SMB — call find_business and verify_business first if needed. COST: from $0.15 per_call_variable (see preview_cost for exact) LATENCY: ~5000ms EXECUTION: async_by_default (use get_outcome to retrieve result)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNo
actionYes
smb_idYes
serviceNo
customerNo
requested_timeNo
idempotency_keyNoOptional client-supplied key for safe retries. Replaying the same key within 24h returns the original receipt - the operation is NOT re-executed and NOT re-charged.
existing_appointment_idNoRequired for reschedule/cancel

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=false, destructiveHint=true, etc.), the description adds significant behavioral context: cost, latency, async execution with get_outcome retrieval, and routing through the SMB's native booking system with fallback to voice AI or web form. This goes well beyond annotation hints and is highly informative for an agent.

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 well-organized with a clear lead sentence, examples, and usage/cost sections. It is somewhat long but each section adds value. The front-loading of purpose and examples helps quick understanding. Slightly more compact could be achieved, but it remains efficient.

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's complexity (8 parameters, nested objects, async behavior, cost, routing), the description covers most essential aspects: what it does, when to use, cost, latency, and async retrieval. It lacks specifics on return format (expected since async, but could mention) and error handling, but overall it is quite complete for an agent to decide and invoke correctly.

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

Parameters2/5

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

Schema description coverage is only 25% (descriptions only for idempotency_key and existing_appointment_id). The tool description does not compensate adequately: it provides examples showing usage of action, service, and smb_id, but does not explain the structure of requested_time, customer, or the relationship between action and existing_appointment_id. With low schema coverage, the description carries the burden and falls short.

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's purpose: 'Availability lookup, hold, confirm, reschedule, or cancel appointments with an SMB.' It specifies the resource (appointments with SMB) and the set of actions, and it differentiates itself from siblings like find_business and verify_business by focusing on appointment scheduling. The examples reinforce the actions and clarify usage.

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?

The description includes explicit 'WHEN TO USE' and 'WHEN NOT TO USE' sections, stating to use for booking/rescheduling/cancelling with a verified smb_id, and not for bulk scheduling or without verification. It also instructs to call find_business and verify_business first if needed, providing clear routing guidance.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool maps to a distinct stage or concern: link preflight vs import, business discovery vs verification, scheduling, async status/outcome, cost preview, and health. The closest pair (check_booking_link and import_booking_url) is explicitly differentiated as a free guard vs actual registration.

Naming Consistency4/5

Nearly all tools follow a consistent verb_noun snake_case pattern such as find_business, verify_business, schedule_appointment, and get_status. The only real deviation is self_test, which is a standard health-check name but not verb_noun.

Tool Count5/5

Nine tools is well-scoped for an appointment-booking service that also needs async operation tracking, cost estimation, and health checks. Each tool has a clear role and none feel redundant or tacked on.

Completeness4/5

The toolset covers the main lifecycle: find or import an SMB, verify it, schedule/cancel/reschedule, poll status, retrieve outcomes, and preview costs. Minor gaps exist—send_message and capture_lead are referenced as downstream operations but not exposed, and there is no direct tool for listing supported platforms.