datasets_starbucks_stores_facets

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

No annotations are provided, so the description carries the full burden. It discloses that it returns 'terms aggregation counts' and lists the facets, indicating a non-destructive read operation. However, it omits details like response format, pagination, or any potential limits. The description adds basic transparency but not rich behavioral 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?

Two sentences, no fluff. The first sentence states action and result; the second covers the facet enum and filter compatibility. Information is front-loaded and every clause 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?

With no output schema, the description is responsible for explaining the return value. 'Terms aggregation counts' is vague—an agent might not know the exact structure (e.g., buckets with keys and counts). It also does not mention pagination or limits. Given the simple nature of the tool, it is adequate but leaves some ambiguity about the output shape.

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 every parameter already has a description. The description adds value by explaining that the filter parameters (q, city, state, market, amenity, country) are shared with the search tool and serve to scope the aggregation. It also enumerates the facet values, reinforcing the required 'facet' parameter. This goes beyond the schema alone.

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's purpose: it facets stored Starbucks stores and returns terms aggregation counts. It lists the exact facet enum (country, state, market, amenities, ownership_type_code), which distinguishes it from the related search, item, and nearby tools. An agent can easily understand what it does.

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 explicitly states it 'Accepts the same filter parameters as search to scope the aggregation.' This clearly implies it is used for aggregating counts over the store directory, with the same filtering as the search sibling. It does not explicitly state when not to use it, but the context is sufficient to route an agent correctly.

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