AI-Process Receipt

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

With no annotations, the description carries the full burden and discloses key behaviors: async execution ('202 + Location poll'), output persistence ('saves process_receipt.json'), parameter defaults, docName resolution, and customFieldKeys merging rules. It omits auth/rate limit details but provides substantial behavioral context beyond the schema.

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 information-dense but presented as a long run-on paragraph with many parenthetical clauses, making it harder to scan. It repeats the tool name and packs several distinct concerns into one sentence, reducing structural clarity despite every part having substance.

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 complex tool with 10 parameters and no output schema or annotations, the description is largely complete: it covers purpose, key parameters, execution flow (202 + Location polling), and the output artifact. Missing details like output_dir semantics and exact return payload are gaps, but the overall context is robust.

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 0%, so the description compensates by mapping API field names, explaining customFieldKeys camelCase and CSV merging, providing receipt_type examples, and stating boolean defaults. It leaves output_dir unexplained, but the majority of the 10 parameters receive meaningful semantic context.

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 clearly states a specific verb and resource: 'extract structured receipt data via PDF4me POST /api/v2/ProcessReceipt.' This differentiates it from sibling document-processing tools by targeting receipts and structured extraction. The redundant title phrase is a minor issue but does not obscure purpose.

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?

The description implies the receipt use-case through 'extract structured receipt data' and the list of receipt_type examples, but it never explicitly states when to choose this tool over related siblings like process_invoice or process_universal_document. There is no mention of exclusions or alternative tools, leaving usage guidance implicit.

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