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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds value by explaining that the tool returns a count (not a quality score), that found:false is never a fabricated count for unknown products, and that low count means not bad, high count means not better. No contradiction.

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 verbose but well-structured. It starts with the main purpose, then lists fields, and ends with usage interpretation. Almost every sentence adds necessary information, though some could be condensed slightly.

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?

Despite no output schema, the description thoroughly explains the return fields (outlets, labs, metrics, mentioned, reading) and their meaning. It also addresses edge cases like unknown products and the interpretation of counts, making it complete for this tool's complexity.

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% with descriptions for both parameters (category and slug, with examples). The description does not add significant new meaning beyond what the schema provides, so baseline 3 is appropriate.

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 it provides a count of evidence for one product, specified by category and slug, and explicitly distinguishes it from a quality score. This differentiates it from sibling tools like get_benchmark and list_coverage.

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 explains when to use the tool: 'Use it to state how much independent hands-on evidence backs any claim about a product.' It also clarifies the meaning of low/high counts and that found:false indicates an unknown product. It does not explicitly list when not to use, but context is sufficient.

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