shopify_analytics_query

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

No annotations provided, so description carries full burden. Describes default behavior and provides example query syntax, but does not explicitly state whether the operation is read-only or disclose potential side effects, rate limits, or data volume considerations.

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?

Four concise sentences: introduction, use cases, default behavior, and parameter explanation. Front-loaded with purpose, no redundancy, every sentence adds value.

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 single-optional-parameter tool with an output schema, the description covers purpose, parameter, and default behavior completely. No gaps in essential information.

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?

Input schema has no description for the 'query' parameter (0% coverage). The description fully compensates by explaining the parameter as 'ShopifyQL statement' with a concrete example, adding essential meaning beyond the raw 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?

Clearly states 'Run a ShopifyQL analytics query against the shop's analytics warehouse' – a specific verb+resource. Lists use cases (revenue, AOV, etc.) that distinguish it from sibling Shopify tools like shopify_get_shop_info or shopify_list_abandoned_checkouts.

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?

Provides explicit when-to-use guidance: 'Use for revenue, AOV, sessions, conversion, and grouped time-series metrics.' Also explains default behavior (net_sales + order_count by day for last 30d) when query omitted. Does not list alternatives or when-not-to-use, but context makes differentiation clear.

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