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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds meaningful behavioral context beyond that: it clarifies that popularity is based on actual captured order units rather than views, ratings, or editorial choices. This helps an agent set correct expectations about the output semantics.

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 with no filler. The core definition comes first, followed by explicit user-intent examples. Every phrase earns its place and the description is immediately scannable.

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?

The tool is simple, has fully documented optional parameters, full annotation coverage, and an output schema. The description adds the only missing semantic detail—what 'best sellers' means operationally—making it complete for selection and invocation.

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%, with both 'days' and 'limit' fully documented including defaults and maximums. The description adds no additional parameter syntax or format nuance; it provides only high-level context about the ranking, so a baseline score of 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 identifies a specific resource ('the store's best-selling / most popular products'), a specific ranking criterion ('real units sold from captured orders'), and concrete intents ('best product', 'most popular', 'top seller'). This differentiates it from sibling tools like list_products and popular_content by scope and metric.

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 maps natural language queries to this tool: 'what is your best product', 'most popular', 'top seller', and recommendation requests. It gives strong positive usage context, though it does not explicitly state when to prefer a sibling like related_products or popular_content.

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