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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description adds genuinely useful context beyond them: it costs nothing, results render as one card per route in a panel in Claude/ChatGPT, and revenue figures are estimated rather than actual. The estimated-revenue caveat in particular prevents misreporting of returned numbers. It stops short of describing volume, ordering, or coverage limits of the Store Finder data.

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 intent is front-loaded and no sentence is purely filler, but the first sentence is an extremely long run-on enumerating six channels plus four metrics, and the all-caps framing adds emphasis without information. The parenthetical '(you can add general advice after)' and the panel-rendering note are useful but interrupt the flow. It is dense rather than tight.

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?

There is no output schema, and the description compensates fully by enumerating what comes back: route groupings, store counts per route, median estimated monthly revenue, number over $5k, and top real examples. Combined with the zero-cost note, the estimated-data caveat, and the panel rendering behavior, an agent has everything needed to call it and present the results correctly.

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 coverage is 100% and the single enum parameter is already documented in the schema as 'Only stores selling this product type,' so the description's line 'Optional category narrows it to one product type' largely restates it. It does confirm optionality and filtering semantics, but adds no meaning beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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 precisely states the resource and the shape of the result: stores from the Store Finder grouped by traffic route, with per-route store counts, median estimated revenue, count over $5k, and top examples. That is far more specific than a name restatement and is clearly distinguishable in content from siblings like digital_product_trends or find_digital_stores. It does not name those siblings explicitly, so sibling differentiation is implicit rather than stated.

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 explicit trigger conditions ('whenever the user asks how to get traffic, visitors, customers or sales...'), a priority instruction ('CALL THIS FIRST'), and post-call guidance ('you can add general advice after'). What's missing is an explicit negative boundary or named alternative (e.g., when to prefer digital_product_trends or find_digital_stores instead), so the agent must still infer routing among related tools.

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