Compare Shopify Customer Growth

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

Annotations already confirm this is a safe read-only, idempotent operation case. The description adds valuable behavioral nuance: that count precision is preserved even when Shopify caps a count. This tells the agent that the output includes a precision flag or similar, which is not inferable from the schema or annotations. No contradiction with 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?

Two short sentences convey the essential purpose and a key output nuance. The core function is front-loaded in the first sentence, and the second adds relevant detail about precision. Every word earns its place; no fluff or repetition.

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?

Given the tool's moderate complexityants, no output schema, and the fact that it returns aggregated counts, the description covers the essential output behavior (comparison across periods, precision visibility when capped). It could be more explicit about the output structure (e.g., whether it returns a table with per-store rows), but the description is sufficient for an agent to understand the deliverable and its caveat. Annotations handle safety, so the description adequately fills in the remaining context.

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?

The input schema already provides full coverage (100%) with descriptions for both parameters: 'days' (period length) and 'stores' (aliases). The description adds no extra parameter detail beyond mentioning 'selected stores' and 'current/previous periods', which maps directly to the schema. Since coverage is high, a baseline of 3 is appropriate.

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 verb (compare), resource (new-customer counts), and scope (across current and previous periods for selected stores). It clearly distinguishes this tool from the many other Shopify reporting tools in the sibling list, which focus on other domains (orders, inventory, catalog, etc.).

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 description makes the comparison goal explicit, so an agent can infer that this tool is for customer-growth analysis rather than other reports. However, it does not explicitly state when to prefer this tool over alternatives (e.g., if a more granular or raw count tool exists), nor does it mention any exclusions. Still, the context is clear enough for typical use.

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