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hairdora

List payments

list_payments
Read-onlyIdempotent

List a bounded page of operational payment records in the authenticated user's organization, most recent first. Returns only the payment, client, and salon references needed for workflow chaining, time, amount, currency, and status. It cannot charge, refund, transfer, or otherwise move funds. Cardholder data, processor/customer ids, credentials, errors, discounts, staff commission splits, operator notes, and organization data are excluded server-side. Optionally filter by salonId, clientId, or time window; hasMore reports truncation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of payments to return (default 20, maximum 100)
endTimeNoOnly payments ending before this ISO 8601 time
salonIdNoFilter by a salon id returned by list_salons
clientIdNoFilter by a client id returned by list_clients
startTimeNoOnly payments starting at or after this ISO 8601 time

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
hasMoreYes
paymentsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds valuable operational details: bounded pagination, most-recent-first ordering, server-side exclusion of sensitive data, and hasMore for truncation. No contradiction; these traits go well beyond 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 description is compact and front-loaded with the core purpose. Each sentence adds distinct information (fields returned, exclusions, filters, pagination) without redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description fully covers what the tool returns, excludes, its ordering, pagination behavior, and operational boundaries. Given the output schema exists, nothing essential is missing 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 100%, so each parameter is described. The description adds that filters are optional and clarifies the pagination semantics (hasMore indicates truncation), which is not in the schema. This goes beyond the baseline and provides useful context for interpreting limit and time-window parameters.

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 ('List') and resource ('payment records'), with a clear scope ('authenticated user's organization'), ordering ('most recent first'), and enumerates the exact fields returned. It also explicitly disclaims any fund-moving capability, distinguishing it from potential payment-mutation siblings.

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 explicitly states when to use it (for workflow chaining of payment references) and what it cannot do (charge, refund, transfer), giving clear when-not guidance. It also notes optional filters, providing usage context without naming sibling tools directly.

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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