Query Store Analytics (ShopifyQL)

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

Annotations already declare readOnly/idempotent/openWorld/non-destructive, so safety is covered. The description adds genuinely non-structured behavior: a syntax error surfaces as an error rather than an empty table, so an agent can distinguish 'broken query' from 'no 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?

Front-loaded one-line summary, then grammar, datasets, examples, and an error caveat. Each example demonstrates a different capability (time grouping, multi-metric + limit, simple scalar), so no sentence is 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?

Despite no output schema, the description tells the agent the return shape (a table) and the error/empty-result distinction. For a single-parameter query tool with full annotation coverage, nothing material 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?

Schema coverage is 100% and the schema only says 'a ShopifyQL query', so the description carries the real semantics: the clause grammar, allowed datasets, and three concrete query templates. That is well beyond the single-line schema hint.

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?

States a specific verb (Run/Query) and resource (ShopifyQL analytics) and a concrete result (a table). It is unmistakably distinct from the CRUD-oriented siblings like shopify_list_orders or shopify_get_products.

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 grammar shape, the enumerated datasets (sales, orders, products, customers) and three worked examples make clear this is the tool for aggregate/analytics questions. It never explicitly says when NOT to use it or which sibling to prefer for raw record listing, so it stops short of full routing guidance.

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