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

Annotations declare readOnlyHint=true, so the bar is lower. The description adds the key behavioral detail that this is a pure data fetch with no AI analysis, which is useful for setting agent expectations. However, it doesn't disclose what happens with invalid/nonexistent ASINs, error cases, or rate limits. Beyond the no-analysis trait, behavioral disclosure is modest, though annotations already cover the safety profile.

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 roughly three sentences with useful content packed efficiently. The 'How to use' section adds real value despite being somewhat long. Front-loads the core purpose clearly. Slightly verbose in the guidance section but every sentence earns its place. Could trim marginally, but overall well-structured.

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 partially compensates by listing what's returned (product rows plus computed aggregates across price/sales/revenue/review distributions, revenue concentration, brand spread). This is genuinely useful. Given the tool's moderate complexity (2 params, clear scope) and the explicit no-AI-analysis boundary, the description is largely complete. Could mention pagination or response size but the explicit ASIN cap bounds expectation.

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 baseline is 3. The asins param is well-documented in-schema (format, range, auto-correction behavior). The description adds the conceptual framing that the set should be treated as a mini-market, but doesn't add parameter-level detail beyond what the schema provides. Marketplace is fully documented by its enum.

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?

Description states a specific verb+resource: fetch raw data across an explicit set of 2-100 ASINs, computing aggregates. It clearly distinguishes from siblings by noting it reasons over raw product rows rather than doing AI analysis, and names analyzeNiche as the keyword-based equivalent. The scope (explicit set vs keyword discovery) is 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?

Provides explicit when-to-use: use for an explicit ASIN set treated as a mini-market. Names the alternative (analyzeNiche) for keyword-based discovery. Gives concrete guidance on what to do with results — segment products, spot demand concentration, flag outliers. The exclusions are explicit and actionable.

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