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

Annotations already cover readOnlyHint/openWorldHint, and the description adds real context: the fixed full-directory fetch, local filter application, dataset size, and nearest-first sorting. It stops short of return-shape or pagination detail, but the output schema covers that.

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?

Front-loaded with the resource and its contents, followed by filter semantics and per-parameter rules in three tight sentences. The 'as of this build' qualifier is slightly awkward but honestly conveys data currency, so it 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?

Annotations carry the safety profile, an output schema handles return values, and the description covers the full directory contents, filtering behavior, and every parameter. Nothing an agent needs to call it correctly 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?

With 0% schema coverage, the description compensates fully: it documents country/state as free-text 2-letter equality filters (not enums, with examples), that lat and lng must be supplied together, and radius_miles' range (1–500) and default (50). All five parameters gain meaning beyond the bare schema.

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 states a specific resource ('lululemon's own complete physical store directory') and enumerates its contents (weekly hours, in-store amenities) plus dataset size. It is trivially distinguishable from the other lululemon siblings (product, category, outfit), which are all merchandise-oriented.

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 clearly explains that all filters are optional and applied locally after fetching the full directory, telling the agent it can call with no arguments or narrow down freely. It does not explicitly name alternatives or exclusions, but no true alternative exists for this brand's store data.

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