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werkstatt-jasper

merit-aktiva-mcp

merit_create_sales_invoice_payment

Record a payment against a sales invoice using customer name and invoice number. Specify amount, payment date, and bank ID or IBAN; partial payments are allowed.

Instructions

Record a payment against a sales invoice (POST /api/v2/sendpayment). Identify the invoice with customerName + invoiceNo. Provide bankId or IBAN (IBAN must match a payment method). paymentDate is ISO YYYY-MM-DD. Partial payments are allowed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ibanNoIBAN of a known payment method
refNoNo
amountYesPayment amount (required)
bankIdNoBank / payment-method GUID
invoiceNoYesSales invoice number (required)
paymentDateYesPayment date YYYY-MM-DD (required)
currencyCodeNoRequired if not local currency
customerNameYesCustomer name (required)
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It adds meaningful detail: the endpoint, invoice identification via customerName + invoiceNo, the bankId-or-IBAN requirement, the ISO date format, and that partial payments are allowed. It does not describe response behavior or error cases, but the core mutation semantics are well disclosed.

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: it opens with the core action and endpoint, then gives only the essential constraints in short sentences. Every sentence adds useful information with no filler or repetition of schema content.

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 mutation tool with no annotations and no output schema, the description covers the key invocation details: endpoint, identification, payment method constraints, date format, and partial payments. The main gap is the absence of any indication of what a successful response looks like, but the provided guidance is sufficient for selecting and calling the tool 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 high at 88%, so the schema already documents most parameters. The description adds valuable relational context beyond the schema: bankId or IBAN are alternatives, IBAN must match a known payment method, and customerName + invoiceNo together identify the invoice. This helps the agent understand how the parameters relate.

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: 'Record a payment against a sales invoice', reinforced by the endpoint POST /api/v2/sendpayment. It clearly identifies the target as a sales invoice payment, distinguishing it from siblings like merit_create_purchase_invoice_payment and merit_create_sales_offer_payment.

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?

The description makes it clear this is for recording payments against sales invoices specifically, and the sibling names help disambiguate it from purchase-invoice and sales-offer payment tools. It does not explicitly state when not to use it, but the context is clear enough that an agent can select it correctly.

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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