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

Annotations already mark the tool as read-only and non-destructive. The description adds meaningful behavior beyond that by explaining interpretive nuances: the data_coverage flag distinguishes a null demand reading as a coverage gap rather than zero sales, and the rating_coverage flag is called out. This helps the agent reason about results more accurately. No contradiction with annotations exists.

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 long but densely informative, and it front-loads the core purpose before enumerating outputs and usage triggers. Every major clause adds useful detail about return values or interpretation. It is somewhat verbose, but for a tool with no output schema this level of detail is justified.

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 burden of explaining return values, and it does so thoroughly: composite score, dimension breakdown, demand estimate with confidence, data coverage flag, rating/review data, brand dominance, red flags, product identity, price, and fulfillment mix. This is sufficient for an agent to select and invoke the tool correctly, though it does not cover error or edge-case behavior.

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%, and all three parameters already have descriptions in the schema. The tool description adds context about the ASIN being a single item and mentions qty for ROI sizing indirectly, but it does not substantially enrich parameter semantics beyond the schema. Baseline 3 is appropriate.

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 opens with a specific verb and resource: 'Evaluate a single ASIN for FBA sourcing.' It clearly differentiates from sibling tools like evaluate_brand and evaluate_category_for_private_label by focusing on a single ASIN, and it names the type of output (sourcing score, demand estimate, red flags). An agent can tell exactly what this tool does without guessing.

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 includes explicit usage triggers: 'Use when the user asks "should I buy this?", "how fast does this sell?", or shares an ASIN and wants a sourcing recommendation.' This is clear contextual guidance, though it does not explicitly name alternatives or state when not to use it, so it falls just short of a 5.

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