datasets_techstack_facets

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

No annotations provided, so the description carries full burden. It clearly states the tool returns distribution counts and that it uses the same filters as search. Additional details like pagination or rate limits are absent but not critical for a facet endpoint.

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, directly stating the purpose, how it works, and listing facet values. No extraneous information; it is front-loaded and efficient.

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 the large parameter set (18) and no output schema, the description adequately explains the tool's function and relationship to search filters. It could mention result structure or limits, but overall it is sufficient for an agent to understand usage.

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 parameters are well-documented in the schema. The description adds minimal value beyond emphasizing the facet parameter and its enum values, which is already listed in the schema description of the parameter.

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 tool facets the website tech-stack dataset and returns distribution counts. It lists the specific facet enum values and notes it honors the same filters as search, distinguishing it from other dataset tools like datasets_techstack_search and datasets_techstack_item.

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 indicates this tool is for obtaining distribution counts ('market-share view') using search filters, implying it is for aggregated data. It lacks explicit 'when not to use' guidance but the context of sibling tools makes it clear that search or item tools are for raw data.

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