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

No annotations are present, so the description must fully disclose behavioral traits. It notes the algorithm (Luhn mod-10) and that the input should be a numeric string, but it does not state the output format (e.g., boolean true/false), error handling for invalid non-numeric input, or side effects. The presence of an output schema (context indicates one exists) partially mitigates this, but the description itself adds minimal behavioral context.

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 a single, well-constructed sentence that wastes no words. It front-loades the core function (validation against Luhn checksum) and provides relevant examples. Every part is purposeful.

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?

The tool is simple (one parameter, known algorithm), and an output schema exists (per context signals), so the description does not need to explain return values. However, it omits usage context relative to sibling tools (e.g., when to pick this over ean_validate) and does not mention any constraints (e.g., length limits for specific formats). The description is minimally adequate for a trivial tool but not fully informative.

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% (the sole parameter 'value' has a description). The tool description adds the context that the string should be numeric and aligns with the Luhn algorithm, which the schemas field lacks. This adds some meaning beyond the schema but does not compensate for missing details like expected length or allowed characters. Baseline 3 is appropriate.

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 the tool validates a numeric string using the Luhn (mod-10) checksum and gives specific examples (payment card numbers, IMEI numbers), making the purpose evident. However, it does not explicitly differentiate from sibling tools like ean_validate or isbn_validate, which also validate specific identifier formats, so a slight deduction applies.

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 that the tool is for Luhn-based identifiers but provides no explicit guidance on when to use it versus alternatives (e.g., ean_validate for EAN codes, iban_validate for IBANs). It lacks 'when-to-use' or 'when-not-to-use' language, leaving the agent to infer context from the examples alone.

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