datasets_airbnb_facets

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

The description discloses that certain facets remain empty until enrichment coverage is high, which is a key behavioral trait. It also mentions suppressed counts, but does not explain suppression reasons or confirm read-only nature. Given no annotations, the description carries the burden and provides moderate context.

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 information-dense and front-loaded with the primary action. It efficiently conveys enums and exceptions without unnecessary repetition. Slightly long but earns its length.

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 lack of output schema and high parameter count, the description explains what the tool returns (suppressed counts) and relates to search, but does not describe the output structure or suppression mechanics. Adequate but has gaps.

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% (baseline 3). The description adds value by explaining facet enum meanings (e.g., 'admin1 (top subdivision)'), listing room_type values, and noting reliability thresholds for certain facets, going 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 tool facets the Airbnb markets dataset and returns suppressed distribution counts. It distinguishes from sibling tools like datasets_airbnb_search by mentioning it honors the same filters, and the dataset focus is explicit.

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 usage for aggregated counts by listing facets and group_by enums, and links to search filters. However, it does not explicitly state when to use this tool over alternatives or provide when-not-to-use guidance.

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