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

Annotations already mark the tool as read-only and non-destructive, so the description does not need to cover safety. It adds useful behavioral context beyond that: results are 'ranked by relevance,' the returned fields are enumerated, and it explains that optional filters 'narrow the result by any returned field.' This is meaningful but does not address pagination, result limits, or other execution behaviors.

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 compact and well-structured: it states the action first, gives usage conditions with examples, then summarizes return fields and filtering. Every sentence adds useful information, and the length is appropriate given the tool's 19 parameters and no output schema.

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 search tool with many parameters and no output schema, the description covers the essential context: what is searched, when to use it, what results contain, and how filtering works. It does not mention pagination, result limits, or the full set of filterable fields precisely, which leaves some ambiguity, but the core calling context is well covered.

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 only 21%, so the description carries real weight here. It helps by explaining the filter model and naming several filter families (price, demand, rating, fulfillment_in) with allowed fulfillment values. However, many parameters remain effectively unexplained—notably rating_count, demand_revenue_30d, fulfillment percentage filters, and product_brand_contains—so the agent is left to guess at their 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?

The description opens with a specific verb and resource: 'Free-text keyword search over Amazon product titles + descriptions.' It clearly delimits the intended query type ('PRODUCT TYPE or keywords') against brand, category, or ASIN searches, but it does not name a specific sibling alternative such as shopping_search, so full sibling differentiation is not achieved.

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 when to use the tool: 'Use when the user names a PRODUCT TYPE or keywords,' and it provides concrete examples. It also gives a when-not condition ('rather than a specific brand, category, or ASIN'), though it does not point to specific alternative tools for those cases.

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