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

Beyond the annotations (readOnlyHint=true, destructiveHint=false), the description adds significant behavioral context: it declares the operation is a pure data fetch with no AI analysis, and it specifies a mandatory output contract for handling 'Account notice:' paragraphs that must be copied verbatim. This is critical, non-obvious behavior that the schema and annotations do not convey, and nothing contradicts the read-only hint.

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 long but every section earns its place: the purpose is front-loaded in the first line, the behavioral note and usage guidance are dense but actionable, and the output contract is mandatory critical information. The structure moves logically from what the tool does, to how to interpret results, to the required reply format, with no filler or redundancy.

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 having no output schema, the description thoroughly explains the return content: top products by revenue, distribution aggregates, revenue concentration, brand spread, and a concrete example field (revenueTop5SharePercent). It also covers the account-notice edge case and gives interpretation guidance, so an agent has enough context to call the tool and process its result correctly.

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 input schema already provides 100% descriptive coverage for all four parameters: keyword, count, filters, and marketplace, including currency context and marketplace rules. The tool description adds no additional parameter-specific meaning beyond what the schema details, so the baseline score of 3 applies.

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 'Market snapshot for an Amazon niche/keyword' and enumerates exactly what is returned: top products by revenue plus computed aggregates across price, sales, revenue, reviews, concentration, and brand spread. This clearly distinguishes it from sibling tools like amzscout_analyze_product or amzscout_compare_niches by scope (single niche, data snapshot) and by the explicit 'Pure data fetch (no AI analysis)' disclaimer.

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?

A dedicated 'How to use' section explains how to interpret the returned data for judging niche attractiveness, covering demand concentration, price bands, review moats, and brand dominance. This provides clear context for when the tool's output is useful, though it does not explicitly name sibling tools to use instead or state when not to use this tool, so it stops short of a 5.

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