LLPRO Campus

Make a PDF invoice from invoice fields, with a signed receipt of its hash

invoice_make

Read-onlyIdempotent

Make a clean PDF invoice from invoice fields, and get back a signed, dated receipt of the PDF's hash, so either side can later show which invoice was made and when. Send: number, issue_date (YYYY-MM-DD), optional due_date, currency (USD, EUR, GBP ... 43 supported), seller {name, address, tax_id, contact}, buyer {name, address, ...}, items [{description, quantity, unit_price}] (up to 40), optional tax_lines [{label, rate_percent OR amount}], notes, payment_terms. Every number, rate and tax line is yours: we only add them up (half-up rounding to the currency's smallest unit); no tax advice, no tax rules, no checking of tax ids. Moves no money and makes no payment link: web addresses and links (dotted names, IP addresses, schemes such as http: or pay:) are refused in every text field. Free, no sign-up, 100 invoices a day per caller. Stores nothing: the signed receipt holds the number, dates, totals and hashes, never names or addresses. Text must use Western European letters (the Windows-1252 set; accented Latin letters, euro, pound, yen). Answer: pdf_base64, totals, signed observation, receipt.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "number": "<number>",
      "issue_date": "<issue_date>",
      "currency": "<currency>",
      "seller": "<seller>",
      "buyer": "<buyer>",
      "items": "<items>"
    },
    "name": "invoice_make"
  }
}

Input Schema

Name	Required	Description
`buyer`	Yes
`items`	Yes
`notes`	No
`number`	Yes	Invoice number: letters, digits, space and . _ / # - (1 to 40).
`seller`	Yes
`currency`	Yes	Three-letter code, for example USD, EUR, GBP, JPY.
`due_date`	No	YYYY-MM-DD, on or after the issue date.
`tax_lines`	No
`issue_date`	Yes	YYYY-MM-DD (2000 to 2100).
`payment_terms`	No	Plain words, for example 'Net 30. Bank transfer, account details sent separately.' No web addresses.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 13, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Goes well beyond the annotations: it discloses statelessness ('Stores nothing'), exactly what the receipt retains versus omits (number, dates, totals, hashes, never names/addresses), half-up rounding behavior, a 100/day rate limit, and a URL-refusal validation rule across all text fields. These are the behavioral traits an agent most needs before calling.

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?

Front-loaded with purpose and return value, then a compact parameter inventory, then constraints. It is dense rather than wasteful, though the single long paragraph makes the validation and storage rules harder to scan than they could be.

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?

For a 10-parameter nested tool with no output schema, the description names the returned fields (pdf_base64, totals, signed observation, receipt), covers encoding restrictions, refusal rules, storage semantics, and rate limits. Little an agent needs to call this correctly is missing.

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 schema coverage at 50%, the description compensates by clarifying currency breadth (43 supported beyond the USD/EUR/GBP examples), the item cap of 40, optional due_date/tax_lines/notes/payment_terms, and tax_lines accepting either a rate or a fixed amount. It still omits some schema-held constraints such as max 5 tax lines and date range bounds, but adds real meaning.

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 ('Make a clean PDF invoice from invoice fields') and adds the distinguishing output ('signed, dated receipt of the PDF's hash'). This separates it from sibling tools like pdf_kit or agentrecord_submit_receipt without the reader opening a schema.

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 context is implied through constraints ('Moves no money and makes no payment link', 'Free, no sign-up, 100 invoices a day per caller'), which tells the agent what this tool is not for. However, no alternative tool is named and there is no explicit when-to-use versus when-not guidance.

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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