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

With no annotations, the description carries the full burden and does well: it discloses that missing fields are reported rather than guessed (a non-obvious behavioral trait), that output is structured JSON, and that each call costs 1 credit. It omits auth/permission requirements and error behavior, keeping it from a 5.

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?

Three tight sentences: what it does, when to use it, what it costs. No filler, and the core capability is front-loaded.

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 single-parameter extraction tool with no output schema and no annotations, the description covers input formats, return semantics, missing-field behavior, and cost. Nothing essential for correct invocation is absent.

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?

Only one parameter exists and schema coverage is 100%, so the schema already documents it as 'The invoice XML, UBL 2.1 or CII'. The description echoes the accepted formats but adds no syntax or size guidance beyond the schema, so the baseline 3 applies.

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 a specific verb (reads/extracts) and resource (UBL or CII Factur-X invoice XML) plus the output form (structured JSON). It is immediately distinguishable from the generate_invoice and validate_invoice siblings, which produce or check rather than extract.

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?

Explicitly gives the usage context: 'Use it to ingest supplier invoices before booking them.' This tells the agent when the tool fits. It stops short of naming alternatives or exclusions (e.g., when to prefer validate_invoice), so it is clear but not fully routing.

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