shopify_graphql_introspect

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is established. The description adds valuable behavioral context: precise cost points for each mode, a caution to use the full schema sparingly, and clarifications on parameter defaults and requirements. No contradiction with annotations, and it goes beyond what annotations alone tell the agent.

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 description is tightly structured: the purpose is front-loaded in the first sentence, followed by a compact cost/usage paragraph, then a clean 'Args:' block. Every sentence adds essential information—no filler, no repetition. The layout makes scanning effortless.

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 read-only introspection tool, the description covers everything needed to call it correctly: mode selection, cost implications, parameter semantics, and references to the shop resolution tool. An output schema exists to handle return shapes. Combined with the annotations, nothing critical 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 description coverage is 0%, leaving the description to carry the full burden. It does this excellently: type_name is explained with a regex and examples, shop is tied to shopify_list_stores with a condition, and api_version is given a default and override instruction. Every parameter is fully documented beyond the raw schema, exceeding the baseline expected for low coverage.

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 ('introspect') and resource ('Shopify store's Admin GraphQL schema'), and clearly distinguishes two modes: fetching a single type vs the full catalog. This unambiguously separates it from siblings like shopify_graphql_query, which performs actual queries. No ambiguity remains.

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?

Explicit guidance on when to pass type_name (cheap, ~50 points) vs omit it (expensive, ~800 points, 'use sparingly'). It also tells when shop is required and references shopify_list_stores. However, it doesn't explicitly name sibling tools as alternatives for other operations, so the when-not-to-use guidance is slightly implicit. Still, the cost/size guidance is strong.

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