datasets_vehicle_listings_facets

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

With no annotations provided, the description carries the full behavioral disclosure burden. It does disclose that the output is a terms aggregation (counts), which is meaningful and more specific than the tool name alone. However, it doesn't state whether the many filtering parameters constrain the facet counts, whether multiple facets can be requested per call, or what the response structure looks like. This is a moderate disclosure level.

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 compact at two sentences and front-loads the core purpose. The first sentence ('Facet vehicle listings dataset') is slightly redundant with the second, but the second sentence plus the facet enum list are information-dense and useful. Overall it is appropriately sized with no filler.

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 tool with 20 parameters, no output schema, and no annotations, the description could do more to explain how the filter parameters interact with the facet aggregation (e.g., do q, make, price range filter the counts?). It also doesn't clarify whether only one facet can be requested per call. The core purpose is clear, but an agent might be uncertain about composing valid calls without more behavioral context.

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 no parameter meaning beyond the schema; it merely repeats the facet enum that already appears in the facet property's description. All 20 parameters are already clearly documented in the schema with field-specific explanations, so the description does not need to compensate.

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 action and resource: 'Returns terms aggregation counts for the vehicle listings dataset.' This clearly separates it from siblings like datasets_vehicle_listings_search (which returns listings) and datasets_vehicle_listings_item (which returns a single item). The facet enum list further pins down the exact fields, leaving no ambiguity about the tool's role.

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 offers no guidance on when to use this tool versus the natural alternatives datast_vehicle_listings_search, item, or price_history. It does not state conditions like 'use this when you need count breakdowns per field' or 'when you don't need the raw listings themselves.' Usage context is merely implied by the word 'facets' rather than explicitly explained.

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