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

Annotations already indicate a safe, read-only, idempotent operation. The description adds valuable behavioral context by explaining the differing data sources between Apple and Google, which is not present in the annotations. It does not describe the return format in detail, but the added store-specific behavior is useful beyond the structured metadata.

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 extremely concise: one sentence with a clear action prefix ('List categories for a store') followed by a brief, relevant explanation of store-specific behavior. Every word earns its place; no fluff or redundancy.

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 simple tool with only two parameters, strong annotations, and no output schema, the description provides sufficient context. It explains the key behavioral difference between Apple and Google, which is the main complexity. A slight gap is the lack of return-type details, but given the tool's simplicity and the annotations' coverage, this is not a major omission.

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 100%, providing basic meaning for both parameters (lang, store). The description adds extra semantic value by explaining how the 'store' parameter alters behavior—Apple returns its static genre map, Google returns its categories() constants. This goes beyond the schema's simple 'Target store' description, justifying a score above the baseline of 3.

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 clearly states the tool's function: 'List categories for a store.' This is a specific verb+resource combination. It also distinguishes the tool from siblings by noting the Apple/Google genre-map difference, making it unique among app-focused siblings like get_app_reviews or search_apps.

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 implicitly conveys when to use the tool: when you need categories for a store, with the store parameter determining the source. It provides clear context for both supported stores (Apple returns a static genre map, Google returns categories() constants), but does not explicitly mention alternatives or when not to use it. This is acceptable for a simple tool.

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