generate_invoice_ae

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

With no annotations, the description carries full disclosure burden. It goes well beyond a basic statement by specifically describing the output dict keys, the IDs applied, and the mandatory UBL elements emitted. It does not cover error handling or validation failure behavior, but provides strong insight into the tool's transformation behavior.

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?

The description is well-structured and efficient. It front-loads the core purpose, then adds meaningful technical details, and ends with a clear bulleted return contract. Every sentence contributes useful information without repetition or filler.

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 tool with two parameters, full schema coverage, and an explicit output dict description, the description is sufficiently complete. It tells the agent what the generated document contains and what the tool returns. It could mention validation of the input data before generation, but the receipt is minor given the provided schema and output description.

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 coverage is 100%, so baseline is 3. The description adds value by explaining that variant drives the selected CustomizationID/ProfileID and that invoice_data should match the AEInvoice schema, including a specific nested field (AEParty.trade_license_number). This clarifies how the parameters relate to the generated XML.

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?

States the specific action 'generate', the artifact 'PINT AE UBL 2.1 e-invoice XML document', and the input 'structured data'. It also names the key distinguishing behaviors — applying CustomizationID (BT-24) and ProfileID (BT-23) — which separates it from sibling parse/validate tools.

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?

Clear context: use this tool when structured invoice data must be turned into a PINT AE UBL XML. It explains the variant selects billing vs selfbilling profiles, giving practical usage direction. It does not explicitly state when not to use it versus parse or validate, but the generation purpose is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.