datasets_airbnb_search

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses multiple behaviors: 'Thin cells are suppressed', 'admin1, locality, room_type and property_type are enrichment-derived and stay empty until their coverage is high enough to be reliable', 'guest_favorite_pct... is an observed lower bound', and the FX conversion detail. This is rich behavioral context that goes beyond what the schema provides.

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 long but every sentence adds value. It front-loads the core purpose and aggregate-only nature, then systematically explains the group_by options, metric definitions, and sort enums. It is dense but not verbose, and it is well-structured with parenthetical clarifications. Each sentence earns its place.

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 tool's complexity (12 parameters, enums, filters, metrics) and the absence of an output schema, the description is remarkably complete. It explains what data is returned (market cells with specific fields), how filters behave, what the metrics mean, the suppression floor, enrichment coverage, and even the FX conversion approach. An agent would have a thorough understanding of how to call this tool correctly.

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?

Although schema coverage is 100%, the description adds substantial meaning beyond the schema. It explains what each group_by enum value means, the semantics of the returned metrics (median_price_usd, guest_favorite_pct, avg_person_capacity), and how to combine filters (e.g., 'combine with group_by=room_type for median price by room type'). It also clarifies the min_listings behavior of raising the suppression floor, which is not evident from 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 states a specific verb ('Search'), a specific resource (the Airbnb markets dataset), and the exact dataset id enum value. It explicitly differentiates from sibling tools by stating 'Aggregate-only: each row is a market cell, never an individual listing.' This is clear and distinguishes it from airbnb_search, airbnb_room, etc.

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 clearly states the aggregate nature and that it never returns individual listings, which implies when NOT to use it. However, it does not explicitly name alternative tools like airbnb_room or airbnb_search, or provide a direct 'use X instead' condition. The context is clear but the guidance on alternatives 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.