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

Annotations only declare readOnlyHint and openWorldHint; the description carries the full burden and exceeds it. It discloses aggregation method, privacy review, minimum account thresholds, per-account/day deduplication, rounding rules, null semantics for no_results_percent, exclusion of anonymous searches, stale-data date behavior, and warns that topic text is untrusted data. This is exemplary behavioral disclosure.

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?

Although dense, every sentence and clause earns its place by defining a critical behavioral caveat or semantic distinction. The core purpose is front-loaded in the first sentence, and the remaining text is a compact list of necessary data-quality disclaimers rather than fluff.

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?

Despite having no output schema, the description is complete enough for an agent to call the tool safely and interpret results: it explains time window, topic generation, privacy thresholds, counting rules, percentage rounding, null cases, provenance of stale data, availability semantics, and untrusted-text warnings. Nothing needed for correct invocation or interpretation 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?

The tool has zero parameters and schema coverage is 100%, so there are no parameter semantics to elaborate. The description instead adds meaning around the output semantics and edge cases, which is the right compensation. A 4 reflects the strong handling of a no-parameter tool.

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 first sentence states a specific verb ('Discover'), a specific resource ('general needs from Agorean searches'), and a precise temporal scope ('last 30 complete UTC days'). It also clearly distinguishes itself from transaction/search tools: this is aggregated market-insight discovery rather than finding listings or users.

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 the tool is appropriate—understanding aggregate demand trends—and includes implicit exclusion guidance such as 'Anonymous searches are not counted' and 'No results means no organic results after filters, not proof that supply does not exist.' It does not explicitly name an alternative tool, but the context sufficiently routes an agent away from the sibling search tools.

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