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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuinely non-schema information: the $0.008 USDC x402 payment flow on Base, the unpaid-call response, and the no-charge-on-invalid-input policy. It stops short of describing output list structure or limits on extracted item counts.

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?

Two sentences, zero filler. The functional statement comes first and the payment/error behavior second, which is the right priority order for an agent deciding whether to call.

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 one-parameter, non-destructive extraction tool with no output schema, the definition covers purpose, cost, and failure modes adequately. A brief note on the returned list shape (e.g. whether matches keep positions or context) would close the remaining gap.

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?

With a single parameter at 100% schema description coverage, the schema already carries the meaning and even includes an illustrative example. The description adds only that the text is scanned for the four entity types, which is marginal value over the schema — 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 ('Extract') and enumerates the exact resources produced (URLs, emails, money amounts, ISO dates) from free text, plus the output shape ('structured lists'). It is clearly distinguishable in practice from siblings like entity_normalize or json_repair, though it never names a sibling to sharpen the boundary.

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?

Gives useful operational context about payment: cost per call, that unpaid calls return the payment requirement, and that invalid input is free. However, it offers no guidance on when to choose this over related siblings (entity_normalize, email_auth_check) or what input shape works best; usage is only implied by the resource list.

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