extract_invoiceExtract the embedded XML and key business terms (number, dates, parties, totals, VAT, lines).
| Name | Required | Description | Default |
|---|---|---|---|
| xml | No | CII or UBL XML as text (alternative to document_base64) | |
| include_xml | No | Include the raw XML in the result | |
| document_base64 | No | Factur-X PDF or XML file, base64 encoded |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only and idempotent behavior, lowering the bar. The description adds useful context about extracting embedded XML and key business terms, but it does not disclose edge behaviors such as handling of both xml and document_base64 being supplied, or behavior when neither is supplied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence with no filler; the core action and extracted content are front-loaded. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only extraction tool, the description plus the fully documented schema and output schema is mostly complete. The only notable gap is that all parameters are optional in the schema and the description does not explicitly state that at least one input source (xml or document_base64) should be provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline applies. Each parameter is already documented (xml vs document_base64 alternatives, include_xml flag), and the description does not add extra meaning beyond what the schema says.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('Extract') on a specific resource (e-invoice data) and enumerates the extracted content (number, dates, parties, totals, VAT, lines). It does not explicitly distinguish itself from siblings like validate_invoice or generate_invoice, but the extraction verb makes the purpose reasonably distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is only implied: an agent can infer this is for pulling structured data out of an e-invoice document. No when-to-use or when-not-to-use guidance is given, and none of the sibling tools are referenced as alternatives.
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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Each tool targets a distinct stage of the Factur-X lifecycle: creation, validation, embedding into PDF, and extraction. There is no meaningful overlap between generate, validate, embed, and extract operations.
All tool names follow a consistent verb_noun snake_case pattern: embed_xml, extract_invoice, generate_invoice, validate_invoice. The pattern is predictable and immediately conveys the action and object.
Four tools is a well-scoped size for this domain, covering the essential operations without redundancy. Each tool earns its place in the set.
The toolkit covers the full core workflow: generate a compliant invoice, validate it, embed the XML in a PDF/A-3 document, and extract previously embedded data. No obvious lifecycle gaps exist for the stated purpose.