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

Beyond the annotations, the description adds useful behavior: the tool is free, it includes known gaps, and shelf-price fallback data is explicitly labeled as Statistics Canada Ontario averages. It does not describe response structure, but the annotations already cover safety and idempotency.

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 definition is short and front-loaded with the covered data categories, followed by the important caveats and the call-to-action. Every phrase contributes useful information.

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 zero-parameter coverage/meta tool, it is largely complete: it explains what is covered, what known gaps exist, and when to consult it. The main minor gap is that it does not describe output shape, though no output schema is supplied.

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?

There are zero parameters, so there is nothing for the description to map beyond the tool itself. The baseline for a no-parameter tool applies, and the description does not create parameter ambiguity.

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 states the coverage categories: banners, current flyer weeks, store counts, and known gaps. It is specific enough to distinguish this metadata/coverage tool from data-search siblings, though it does not name those siblings directly.

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?

It gives an explicit usage rule: call it before telling a user something is not on sale. It does not name alternative tools or describe when not to call it, but the main negative-claim scenario is clear.

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