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

Annotations already declare read-only, non-destructive behavior, and the description adds substantial context: it is a paid $0.01 x402/USDC-on-Base endpoint, uses the latest reported quarter only, compares same weekdays year earlier, and is same-store/like-for-like. These details go well 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 core metric is front-loaded, but the description is a single long, dense sentence with many nested clauses. The information is relevant, but the structure makes it harder to parse than necessary.

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?

For a one-parameter metric tool with no output schema, the description provides extensive context about timing, comparison method, cost, and data scope. It is largely complete for invocation, though it does not fully clarify the return structure implied by 'vs_market_*'.

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 the single 'company' parameter is fully documented in the schema with ticker and ISIN formats. The description does not add parameter-specific meaning beyond what the schema already provides, so the baseline score 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?

The description clearly identifies the metric being returned: year-on-year growth in point-of-sale sales and units for a company's latest reported quarter. It is specific about the computation and scope, but it does not explicitly distinguish this tool from sibling tools such as quote_sales.

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?

There is no explicit guidance on when to use this tool versus alternatives like quote_sales or get_sample. The phrase 'vs_market_*, read it first' is a vague prerequisite reference, not a clear usage rule or exclusion.

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