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Mindbody MCP by usefulapi

List appointments

mindbody_list_staff_appointments
Read-only

List booked appointments in a date range, by staff member, client or appointment id — the appointment book. Mindbody: GET /appointment/staffappointments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoPage size (request.limit), 1-200. Mindbody defaults to 100.
offsetNoPage offset (request.offset). Mindbody defaults to 0. See PaginationResponse.TotalResults.
end_dateNoEnd of the range (default start_date) — ISO 8601, e.g. 2026-10-01 or 2026-10-01T09:00:00.
client_idNoOnly this client's appointments.
staff_idsNoOnly these staff ids (omit for all staff).
start_dateNoStart of the range (default today) — ISO 8601, e.g. 2026-10-01 or 2026-10-01T09:00:00.
location_idsNoOnly at these location ids.
appointment_idsNoOnly these appointment ids.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the safe-read nature is covered. The description adds the appointment-book framing and the upstream endpoint mapping, but discloses no pagination behavior, default handling, or result format, so it contributes moderate value beyond structured fields.

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?

A single front-loaded sentence carrying the resource, scope and filters, followed by a compact endpoint reference. No filler sentences; every clause 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 8 fully-described parameters, readOnly annotations, and no output schema to explain, the description covers what an agent needs to invoke the tool. Minor gap: no mention of pagination behavior, though the schema's PaginationResponse.TotalResults note compensates.

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

Parameters3/5

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

Schema description coverage is 100%, so every parameter (limit, offset, dates, client_id, staff_ids, location_ids, appointment_ids) is already documented in the schema. The description only restates the filter axes without adding format or default semantics, so baseline 3 applies.

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?

States a specific verb (List) and resource (booked appointments) with the filtering axes (date range, staff, client, appointment id) and an anchoring phrase, 'the appointment book'. It does not explicitly name or distinguish itself from similar siblings like mindbody_get_class_visits or mindbody_list_client_visits, so a 4 rather than a 5.

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?

Usage is implied by the filter list (you'd use it to find appointments for a staff member, client, or id), but there is no explicit when-to-use vs alternatives guidance and no prerequisites or exclusions. Adequate but leaves routing to the agent's inference.

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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