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

Annotations already declare readOnly/destructive/openWorld, but the description adds substantial context beyond them: the source-preference resolution order (Catalog Items row, then marketplace Listings Items quality snapshot, then seller listing-offer data), the fact that per-source labels and snapshot dates are returned, the two BSR grains explained with their Amazon semantics, and the nullability caveat. This is exactly the behavioral disclosure a snapshot tool needs.

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 paragraph is long but front-loaded with purpose before the exclusion list, and nearly every sentence adds decision-relevant information (resolution order, BSR grains, nullability). It could be scanned more easily with structure, but there is little pure filler.

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 an output schema present, the description need not describe return values, and it fully covers provenance, freshness signaling, BSR interpretation, and null handling. An agent has everything needed to call it correctly and interpret results.

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 schema already documents asin, include_raw_rows, and include_diagnostics. The description contributes nothing about the parameters themselves, so the baseline 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?

States a specific verb and resource ('materialized product metadata for one ASIN') and enumerates the exact fields returned (title, brand, bullet points, dimensions, BSR, etc.), so an agent can distinguish it from siblings like get_listing_registry or get_sales_ranks without opening a schema.

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?

Explicitly states when to use it, what it is not ('captured snapshot data, not a live Catalog Items response'), and names the alternatives with conditions: 'Do not use for seller SKU mapping, sales, inventory, or ad performance; use get_listing_registry with view= listings or the relevant performance tool.' Nothing is left to inference.

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