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ApparelHub-AI

apparelhub-mcp

Official

record_order_payment

Mark an order as paid by recording a manual payment when payment is pending; use sales_channel for storefront-paid orders or stripe for ApparelHub card flow. Does not charge a card.

Instructions

Record a manual payment on an order that is awaiting payment (payment_status="pending"). Use payment_method="sales_channel" for an order already paid on its storefront (Shopify/WooCommerce/Wix — the channel is the source of payment), or "stripe" for an order taken through ApparelHub's own card flow. This marks the order paid; it does not charge a card.

[#81d42b]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
amountNoOptional amount for the caller's intent; the recorded amount comes from the order total.
workspaceNoWorkspace uuid to scope to (agency accounts). Omit for the Default workspace.
order_uuidYesThe order uuid (from list_my_orders).
payment_methodYese.g. "sales_channel" (paid on the storefront) or "stripe" (ApparelHub card flow).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.15.2

TDQS

A4/5.0
Behavior4/5

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

With only openWorldHint in the annotations, the description carries most of the burden, and it does real work: it clarifies the operation records state rather than charging a card ("it does not charge a card") and states the required order status. It still omits reversibility, idempotency, and permission requirements, so not a 5.

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 content is front-loaded and dense, with the scoping condition and the method-selection rule each in their own sentence. The trailing artifact "[#81d42b]" is stray noise that slightly detracts from an otherwise clean structure.

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?

For a state-mutating tool with no output schema, the description covers purpose, precondition, no-charge behavior, and method semantics adequately. It leaves out what happens downstream (e.g., fulfillment) and error conditions such as recording against a non-pending order.

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 the baseline is 3, but the description adds meaning beyond the schema by explaining the semantic distinction between the payment_method values with concrete scenarios rather than the schema's bare "e.g." examples.

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?

The description names a specific verb and resource ("Record a manual payment on an order") and states the precondition (payment_status="pending"), which lets an agent identify the operation. It does not explicitly distinguish itself from close siblings like set_order_payment_method or mark_order_no_payment, so it falls short of 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear when-to-use context (order awaiting payment) and explains which invocation applies for each real-world scenario (storefront-paid vs. ApparelHub card flow). It lacks an explicit when-not or a named alternative tool, which keeps it below a 5.

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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