datasets_producthunt_makers_facets

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

With no annotations provided, the description carries the full burden. It discloses the core behavior: returns distribution counts and honors the same filters as search. It also lists the facet enum values. It does not mention pagination, response format details, or error conditions, but for a simple facet tool this is adequate. No contradiction with annotations (none exist).

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 two sentences with no wasted words. The primary purpose is front-loaded, and the facet enum values are listed in the second sentence. It is compact and structured effectively for an agent to parse quickly.

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 facet tool with a clear schema and no output schema, the description covers the essential aspects: what it operates on, that it returns counts, and that it shares filters with search. It does not describe the exact shape of the return value (e.g., list of facet values with counts), but the phrase 'distributes counts' implies this. Given the simplicity and the schema coverage, it is fairly complete, though a mention of the output structure would push it to 5.

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 description coverage is 100%, so the baseline is 3. The description adds minimal parameter context beyond the schema, only stating that filters are 'the same as search', which gives a general sense of how q, topic, min_products, and min_total_votes work. This does not significantly exceed the schema's own descriptions, so a 3 is appropriate.

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 makers dataset', and specifies it returns distribution counts. It also distinguishes from the sibling search tool by noting it honors the same filters but is for distribution counts. The dataset id and facet enum values are given, making the purpose unambiguous.

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 implies this tool is used when you want facet counts rather than records, and mentions 'honoring the same filters as search' which gives some guidance on parameters. However, it does not explicitly state when to prefer this over search or other facet tools, nor does it provide when-not-to-use conditions. The guidance is implicit rather than explicit.

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