Skip to main content
Glama

Appointment Booking

by dev.hatchloop

preview_cost

Read-onlyIdempotent

Return an expected cost estimate, latency estimate, and success-probability estimate for a proposed call before execution. Returns the exact price when it is fixed, and a min/max range when the cost depends on channel or outcome. It does not promise an accuracy percentage - check cost_range.

EXAMPLE USER QUERIES THAT MATCH THIS TOOL: user: "How much will this SMS cost me?" -> call preview_cost({"operation": "send_message", "params": {"preferred_channel": "sms"}}) user: "Estimate the cost of booking via voice fallback" -> call preview_cost({"operation": "schedule_appointment", "params": {"preferred_channel": "voice"}})

WHEN TO USE: Use before any operation when the agent is operating under a budget constraint and needs to decide whether to proceed. WHEN NOT TO USE: Do not use in a hot loop — cache the result for at least 60 seconds if repeating the same preview. COST: free - no key required LATENCY: ~100ms

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYesThe same request body you would pass to the operation
operationYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark it readOnly and idempotent, and the description adds complementary context: it does not promise an accuracy percentage, returns exact vs range, requires no key, and has ~100ms latency. No contradiction with annotations.

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 core behavior is front-loaded, and the formatted examples, usage guidance, cost, and latency each add non-redundant information. It is longer than minimal but every section earns its place.

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 covers return behavior (fixed vs range, no accuracy promise) and operational constraints (cache, key-free, latency), but it does not spell out the exact output field names beyond cost_range. This is a minor gap in an otherwise complete definition.

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?

With 50% schema coverage (operation is undescribed), the examples compensate by showing valid operation values and the nested params shape. It could go further by enumerating valid operation names or constraints, but it adds substantial meaning beyond the bare schema.

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 states a specific verb and resource: it returns cost, latency, and success-probability estimates for a proposed call before execution, and even clarifies fixed vs min/max pricing. This clearly distinguishes preview_cost from its siblings, none of which are preview/cost tools.

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?

It provides explicit WHEN TO USE ('under a budget constraint...before any operation') and WHEN NOT TO USE ('do not use in a hot loop...cache for at least 60 seconds'). The example queries also give concrete triggers.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Appointment Booking toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources