datasets_producthunt_trends_facets

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

With no annotations, the description carries full burden. It discloses that counts are 'suppressed' and that filtering mirrors search, but does not explain suppression details, rate limits, or other behavioral traits.

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 sentences concisely convey purpose and context. Could be slightly tighter by removing the explicit facet enum listing, but overall efficient and front-loaded.

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?

No output schema, so description should detail return values. 'Returns suppressed distribution counts' is vague; lacks specifics on response structure, pagination, or limits. Adequate for basic understanding but not complete.

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 the baseline is 3. The description repeats the facet enum values and mentions 'same filters as search', but adds no new meaning beyond the schema.

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 clearly states the verb 'Facet' and the resource 'Product Hunt trends dataset', and specifies it returns 'suppressed distribution counts'. It distinguishes from sibling 'datasets_producthunt_trends_search' by indicating aggregation vs. search results.

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 states it 'honors the same filters as search', implying when to use this tool (for aggregated counts) vs. the search sibling. It lists the facet enums (topic, launch_year), but does not explicitly exclude scenarios or mention alternatives.

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