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

Annotations already cover safety (readOnlyHint=true, openWorldHint=true), so the bar is lower. The description adds genuine behavioral context that the annotations do not: the dataset is "periodically rebuilt" (a freshness/staleness caveat) and the operation is a count rather than a record fetch. It does not address rate limits or whether counts span the whole match set or a page.

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 tight sentences with the purpose front-loaded and the routing/positioning detail second. No filler, no restatement of the name.

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?

An output schema exists, so return values need no explanation, and the workflow context is complete. The one gap is that the schema exposes page/page_size for what is described as a count operation, and the description never clarifies whether aggregation covers the entire matching set or a single page.

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 the baseline is 3; every one of the 22 parameters, including the required facet enum list, is documented in the schema. The description only adds the meta-point that the filter set mirrors datasets_google_map_search, which is mild added value rather than new syntax or semantics.

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?

States a specific verb and resource — "Count facet values for matching businesses" — and names the dataset it operates on. It is clearly separable from datasets_google_map_search (returns records) and datasets_google_map_item (single record) by virtue of being an aggregation/count operation.

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?

Explicitly positions the tool in the workflow ("inspect facet distributions before retrieving individual business records") and ties its filter semantics to a named sibling ("apply the same search filters as datasets_google_map_search"). An agent knows both when to reach for it and what to reach for afterwards.

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