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

Annotations already mark the tool read-only and non-destructive, and the description adds meaningful behavioral detail: pure data fetch with no AI analysis, the exact failure mode when marketplace is omitted, local-currency semantics, and the mandatory account-notice output contract. This goes well beyond what annotations alone convey, and nothing contradicts the annotations.

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 front-loaded with the core purpose and organized into clear sections: purpose, usage guidance, marketplace handling, and output contract. It is longer than strictly necessary, but most sentences carry either invocation-relevant or analysis-relevant value, so it earns a strong score rather than a perfect one.

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?

With no output schema, the description carries the return-value burden, and it does so explicitly by listing product rows plus aggregate distributions and by defining the account-notice output contract. It also covers marketplace prerequisites, the no-marketplace failure mode, and the expected analytical posture, leaving no obvious gap for an agent to call the tool 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?

Schema description coverage is 100%, so the baseline is 3, but the description adds real value beyond the schema: do not assume the US marketplace, ask once and reuse the chosen marketplace, and expect no results when marketplace is absent. It also orients the agent on how to reason about the ASIN set as a market segment rather than a flat list.

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 states a specific operation: retrieve raw data for an explicit set of 2–100 ASINs and return product rows plus computed aggregates. It also explicitly positions itself as a pure data fetch with no AI analysis and names analyzeNiche as the keyword-discovery equivalent, so an agent can distinguish it from siblings without opening schemas.

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?

It gives clear context for when to use the tool: when an explicit set of ASINs should be treated as a mini-market, and it directs agents away to analyzeNiche for keyword-based discovery. However, it does not spell out when to prefer related siblings such as compare_products or analyze_product for single-ASIN analysis.

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