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

Annotations declare readOnlyHint=true and idempotentHint=true, so the description does not need to repeat safety info. It adds value by clarifying the source of data ('from the analytics snapshots'), which hints at potential staleness and distinguishes it from real-time sources. This is useful context beyond 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 a single, well-structured sentence that leads with the core purpose, then enumerates key data points, and ends with the data source. Every clause adds information; no fluff.

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 tool's moderate complexity and the presence of an output schema, most return details are covered. The description adequately sets expectations for what fields are returned (catalogue price, variant pricing, COGS, 90-day trend) and the parameter semantics are clear. A slight gap is not explaining that the data may be based on snapshots, but that is hinted.

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?

While the schema already covers the parameter at 100% coverage, the description adds critical disambiguation: the product_id must be the internal MerchantFlow id, not the commerce platform's. This is exactly the kind of semantic context that prevents misuse.

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 ('Get') and a precise resource ('detailed profitability for a specific product'), then lists the exact data returned: catalogue price, variant-level pricing, COGS, and 90-day trend. This clearly distinguishes it from siblings like get_customer_detail or get_top_products, which target different entities.

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 implies when to use it: when needing product-level profitability details, as opposed to aggregate or trend views. It doesn't explicitly state when not to use it or name alternatives, but the specificity is strong enough for an agent to infer the right context.

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