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

The description adds substantial behavioral detail beyond annotations: it explains the de-identification guarantees, pricing model, minimum per pull, accepted payment methods, and the 402 response with a price and preview count when no payment is provided. This is exactly the kind of non-obvious behavior an agent needs to know.

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 dense but every sentence adds value: dataset contents, privacy guarantees, filtering, pricing, payment, failure response, and a link to the sample/schema. The most important information is front-loaded, and there is no redundant filler.

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 tool with 11 parameters, no output schema, and payment-related behavior, the description covers the critical invocation details: what data is returned, how to paginate, how to pay, what happens without payment, and where to find the full schema and sample. Nothing essential is missing for a competent agent to call this tool correctly.

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 schema description coverage at only 36%, the description compensates by mapping filter dimensions (state, since, until, door, vendor, limit, offset) and payment mechanisms (buyer_key, x402, MPP, AP2) to parameter groups. It does not spell out date formats or allowed values, but it gives enough semantic grounding for correct invocation.

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 title and description clearly identify the resource as 'de-identified demand records' and the operation as purchasing/retrieving that dataset. The description specifies exactly what the dataset contains and how it is filtered, distinguishing it from sibling tools like get_quotes or pull_requests.

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 provides clear context for when to use this tool: when the agent needs de-identified auto-insurance demand records, with filtering by state/date/door/vendor and payment. It does not explicitly name alternative tools or state when not to use it, but the usage context is unmistakable.

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