Pricing Unit Economics

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

Annotations declare readOnlyHint=false and idempotentHint=true; the description usefully discloses the concrete write target (blocks of store/pricing.md), which annotations alone don't convey. It stops short of saying whether existing blocks are overwritten or what permissions are needed, so 4 rather than 5.

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?

Three tightly folded lines with zero waste: purpose first, then use-when/alternative, then return shape. Front-loaded and scannable.

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?

An output schema exists, so the return detail is optional, yet the description still summarizes {ok, products:[{margin,...}], path}. The write side effect is noted; only the overwrite/permission behavior is left implicit, which is minor for a tool this well-scoped.

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%, so the schema already documents app_dir, apple_cut, cost_text and cost_photo thoroughly. The description's 'measured AI cost per call' and 'usage profiles' loosely map to these params but add no syntax or default guidance beyond the schema, so baseline 3 applies.

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?

Specific verb+resource ('Compute unit economics') plus the concrete inputs (measured AI cost per call, usage profiles) and the side effect (writing blocks of store/pricing.md). This is directly distinguishable from the sibling aso_unit_economics.

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 'Use when: pricing a built app with real eval data' and a named alternative ('for quick what-if numbers at idea stage use aso_unit_economics'). Both the when and the when-not-with-alternative are stated.

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