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

With annotations already covering read-only, non-destructive, idempotent behavior, the description adds useful detail: the return shape {ok, normalized?, brand?, last4?, issues} and the important non-goal that this is not an authorization or fraud check. It also reinforces the synthetic-number constraint.

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 compact sentences with no waste. The core purpose is front-loaded, followed by usage context and return/non-goal information.

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 validator with no output schema, the description supplies the missing return shape and clarifies the scope and limitations. Annotations cover safety, and the input schema covers the parameter, so nothing essential is missing.

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, but the description adds meaning beyond the schema by specifying that the input number is checked for brand, length, and Luhn checksum. It does not add format syntax or examples, but it clarifies what the examination actually does.

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 gives a specific verb (Check) and precise resource scope: payment-card brand, length, and Luhn checksum. This clearly separates it from the many is-*-safe validators and the generate-* tools in the sibling list.

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?

It states a concrete use case (synthetic card numbers in payment-form tests) and an explicit non-goal (not payment authorization or fraud check). It does not name an alternative tool for generating test numbers, but the positive and negative usage boundaries are clear.

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