What agents are asking for

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

Goes well beyond the readOnly/idempotent annotations by disclosing the data window lag, the delay_hours and window_ends_at fields that reveal it, exclusion rules (test traffic, sellers searching their own listings, identifying terms), the minimum-distinct-IP floor, and critically that client counts are IP ceilings rather than buyer counts. This is exactly the interpretive context an agent needs and cannot get from structured fields.

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?

Purpose and interpretation caveats are front-loaded, and each clause carries real meaning for reading the data. It is dense, with nested parentheses and semicolon chaining, but almost nothing is filler; a lighter structure would help readability without shortening it.

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 whose output is easy to misread (rankings driven by IP-capped client counts), the description supplies the measurement caveats, window semantics, exclusion rules, and paging behavior. With an output schema already present, return-field documentation is not required, yet it names key fields anyway.

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 limit and offset are already documented, and the description largely restates them plus the default (limit 25) and the next_offset convention. It adds only marginal meaning over 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?

The opening sentence states the resource precisely: aggregated search terms that agents used in this directory, plus the decision it supports (which x402 service to build or list). It is not a clean verb+resource phrasing and never names its siblings, but the sibling set (find_paid_service, get_service_details, resolve_endpoint) is unrelated enough that confusion is unlikely.

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 gives clear intended use — deciding what service to build or list — which is a genuine when-to-use signal for a demand-research tool. It adds a useful caveat ('A term being asked does not mean nothing serves it') to prevent misreading demand as a gap, but names no explicit alternatives or when-not conditions.

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