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

The readOnlyHint annotation already declares a safe read operation, so the bar is lower. The description adds value by explicitly stating 'Pure data fetch (no AI analysis)', which sets clear expectations that no interpretation or recommendations will be provided — a key behavioral distinction given the sibling analyze/agent/recommend tools exist. It also notes 'where available' qualifying data completeness.

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 somewhat dense, packing dual-scope explanation, returned fields, and usage guidance into a fairly long paragraph. The 'How to use' section is useful but could be tightened. It's not wasteful, but a two-part structure (what → how) rather than one flowing sentence block would improve scannability.

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 read-only data fetch with 100% schema coverage and no output schema needed, the description is complete. It explains scope selection, returned data, and practical application. The readOnlyHint annotation plus the explicit 'no AI analysis' note fully satisfy behavioral expectations for a fetch tool.

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%, so the schema already documents all three parameters. The description adds value by explaining the semantic relationship between asin and keyword asmutually exclusive scope selectors (ASIN-scope vs keyword-scope) and the functional difference between them — which the schema alone does not convey, since it presents them as independent params.

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 the dual scope (ASIN-scope for product ranks, keyword-scope for niche search data), explicitly notes it's a pure data fetch with no AI analysis, and lists returned fields (search volume, CPC, competition). This distinguishes it from sibling analysis tools like amzscout_analyze_product and amzscout_analyze_niche which imply deeper AI-driven analysis.

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 'How to use' section gives concrete guidance: picking high-volume/low-competition terms for SEO/PPC, using CPC as ad-cost pressure, summing volumes for niche demand, and checking organic vs sponsored ranks for ASIN scope. It lacks explicit 'when NOT to use' or named alternatives, but the dual-scope explanation gives clear situational context distinguishing it from compare/analyze siblings.

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