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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, so the description does not need to restate safety. It adds useful scope by saying the counts cover published entities and that it returns a freshness timestamp, but it does not disclose additional operational behavior such as response format or caching.

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?

It is a single efficient sentence with the core purpose front-loaded and no redundant text. Every part contributes to what the tool returns.

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 zero-parameter, read-only statistics tool, the description covers the key returned metrics and data freshness, and annotations cover the safety profile. The exact response shape is not specified, but the essential information an agent needs to call it correctly is present.

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?

The tool has zero parameters, so there are no parameter semantics to explain; a baseline of 4 is appropriate. The description does not need to compensate for any schema gaps.

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 uses a specific verb and resource: it says it gets counts of published companies, places, and categories plus when data last changed. That clearly distinguishes it from list/search tools in substance, though it does not explicitly name siblings or contrast itself with them.

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?

Usage is implied by the aggregate counts and freshness data: an agent can infer this is the tool for summary statistics rather than listings. However, there is no explicit when-to-use guidance, no exclusions, and no named alternative among get_listing, list_categories, list_places, list_sources, or search_listings.

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