datasets_techstack_facets

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

With no annotations, the description carries the full burden and does disclose the key behavior: it returns counts, applies the same filters as search, and is scoped to dataset id techstack. It does not mention output format, limits, pagination, default run handling, or explicitly state that the operation is read-only, which leaves some behavioral uncertainty.

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 compact sentences with no filler: action, output, filter relationship, and facet enum are all present. The most important information is front-loaded before the repeated enum list.

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?

Given no output schema and 19 parameters, the description cannot explain every filter, but it doesn't need to because the schema is fully described; it supplies the missing linkage to search and the facet choices. The main gap is the lack of any detail about the structure of the returned counts, though 'distribution counts' is a reasonable high-level answer.

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 adds value by pointing out that all filter parameters behave exactly as in search and by listing the valid facet enum values, which helps the agent choose the facet dimension beyond the raw schema listing.

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 opens with a specific action ('Facet the website tech-stack dataset') and states the concrete output: distribution counts over the tech-stack index. It distinguishes this from search by framing it as the technology/category market-share view, and it enumerates the facet dimensions. None of the sibling dataset tools describe this exact aggregation behavior.

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 clearly sets the use case: produce market-share/distribution counts over a dataset rather than raw records, and it says the filters are the same as search. It does not explicitly name datasets_techstack_search as the alternative or state when not to use facets, so it stops short of a 5.

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