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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful scope context ('live' Shopify stores, aggregate breakdowns), but says nothing about data freshness, whether the result is a fixed report or paginated, or behavior on an invalid slug — so it only modestly exceeds what the annotations provide.

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 sentences, both front-loaded with the most important information: the first states what the tool reports, the second supplies the input format. Long enumeration in sentence one is justified because there is no output schema to carry it, and there is no filler.

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?

With no output schema, the description correctly spells out the return dimensions, and it fully covers the one input parameter. Remaining gaps are minor but real: nothing on data recency, no mention of limits on returned lists (top countries / most visited stores), and no error behavior for malformed slugs.

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 0%, so the description carries the full burden for the single required slug parameter and does so well: it gives the exact construction rule for all three case types — app key with hyphens (judge-me), theme name + '-theme' (dawn-theme), pixel key (meta). That is concrete, actionable format guidance the schema does not supply.

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 gives a concrete verb (counts/reports usage) plus the resource (live Shopify stores running an app, theme, or pixel) and even enumerates the specific outputs — category share, top countries, co-used apps, top stores. This makes it unmistakably distinct from find_stores, lookup_store, and weekly_changes, which are store-level or diff-oriented rather than technology-penetration analytics.

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?

Usage is only implied: the slug conventions signal that this tool is for app/theme/pixel level aggregate questions, but no alternative is named and there is no when-to-use/when-not statement relative to the sibling tools. Adequate but leaves the agent to infer the boundary.

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