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

The readOnlyHint annotation already declares this is a safe read. The description adds value by clarifying it returns raw data without AI analysis, indicating that competition data is provided 'where available', and explaining how to interpret organic vs sponsored ranks. This goes beyond the annotation without contradicting it.

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 moderately sized but well-structured: it front-loads the core purpose, then details return data, clarifies the non-analytical nature, and offers a compact 'How to use' list. Each sentence contributes useful information without redundancy, though it could be slightly tighter.

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?

Given the simple optional parameters and no output schema, the description sufficiently covers what the tool returns and how to use it. It implies mutual exclusivity of the two scopes, mentions data availability caveats, and provides practical interpretation tips. The main minor gap is no explicit behavior when both asin and keyword are provided, but the 'either... or...' phrasing largely addresses this.

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 baseline is 3. The description adds meaningful context beyond the schema by explaining the two usage modes (ASIN-scope vs keyword-scope) and linking parameter choices to strategic use cases like spotting listing-optimization gaps and gauging niche demand. This enriches the bare parameter definitions.

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 specifies the tool's function: retrieving Amazon keyword/SEO/PPC data for either an ASIN or a keyword. It names the returned fields (search volume, CPC, competition) and distinguishes itself from siblings by stating 'Pure data fetch (no AI analysis)', making the purpose unambiguous.

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 provides concrete guidance on how to use the data (e.g., 'pick high-volume / low-competition terms for SEO and PPC targeting') and clearly states it is a raw data fetch without AI analysis, implying that analysis tools are alternatives. However, it does not explicitly name those sibling tools or give exclusions beyond 'no AI analysis'.

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