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extract_invoice

Extract structured JSON (parties, lines, totals, payment data) from an e-invoice: XRechnung UBL/CII XML or a ZUGFeRD/Factur-X PDF (embedded XML is pulled from the PDF). Send exactly one of xml / pdfBase64. 5 credits, requires an API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
xmlNo
pdfBase64NoBase64-encoded PDF, max ~2 MB

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses cost (5 credits), authentication requirement (API key), accepted input formats, and behavior for PDFs (embedded XML is pulled from the PDF). This provides meaningful behavioral context beyond the schema, though it does not cover failure modes or output format details.

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 two concise sentences that pack substantial information: purpose, accepted inputs, output type, parameter exclusivity, cost, and auth requirement. There is no filler or unnecessary 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 tool with two parameters and no output schema, the description covers the essential context: what it does, what inputs it accepts, how it processes PDFs, and usage constraints. It does not describe error handling or sample response structure, but the output is clearly summarized as structured JSON with specified fields, making the description largely complete.

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 description coverage is only 50%; the xml parameter has no schema description. The description compensates by explaining that xml represents XRechnung UBL/CII XML and pdfBase64 is for ZUGFeRD/Factur-X PDF, and by enforcing the mutual exclusivity constraint. The pdfBase64 max size is already in the schema, but the format mapping adds value.

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 uses a specific verb ('Extract') and a clear resource ('structured JSON... from an e-invoice'), listing concrete data components (parties, lines, totals, payment data). It also differentiates itself from siblings by focusing on extraction rather than generation or validation.

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 clearly indicates when to use the tool: when you need to extract data from an XRechnung XML or ZUGFeRD/Factur-X PDF. It also states the critical usage constraint 'Send exactly one of xml / pdfBase64.' However, it does not explicitly mention when not to use it or compare to the sibling tools by name.

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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: extraction, generation, and validation of e-invoices. There is no overlap in functionality, making it easy for an agent to select the right tool.

Naming Consistency5/5

All tool names follow the verb_noun pattern (extract_invoice, generate_xrechnung, validate_invoice), which is consistent and predictable. The naming clearly indicates the action and the object.

Tool Count5/5

With 3 tools, the server is well-scoped for its purpose of processing e-invoices. The count is neither too thin nor too heavy, covering the essential operations without redundancy.

Completeness5/5

The tool set covers the full lifecycle of e-invoice processing: creation (generate), extraction (extract), and validation (validate). There are no obvious dead ends or missing critical operations for the stated domain.

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