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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the description only needs to add behavioral context. It adds the classification output categories and the units-sold basis, but it doesn't disclose thresholds or any additional behavior; this is acceptable given the output schema but not rich.

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?

One tight sentence front-loads the verb and finishes with the output classes; no filler or redundancy. Every word earns its place.

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?

Despite its brevity, the description is complete for this simple tool: 2 optional params fully documented in the schema, an output schema for return values, and annotations for safety. No agent-required call detail is missing.

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 documents limit and timeframe defaults, enums, and bounds. The description's 'selected timeframe' merely paraphrases the schema and adds no new parameter-level detail.

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 names a specific action ('Rank products'), the resource ('products'), the metric ('units sold'), and the time scope, and it gives concrete output classes ('fast, steady, slow, or dead'). It does not explicitly contrast with siblings such as get_top_products or run_dead_stock_report, so it stops short of full differentiation.

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 operation itself makes the use case inferable: if an agent needs a units-sold velocity ranking and classification, this tool fits. However, it gives no when-to-use or when-not-to-use guidance and no reference to sibling alternatives, leaving selection partly to inference.

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