set_privacy_nutrition

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

With no annotations provided, the description transparently discloses the key behavior: it does not actually set the label via API but returns instructions and a deep link. It also notes the 'Pro feature' limitation. This goes well beyond the typical description, though it could mention permissions or error handling to be fully complete.

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, using only 3-4 short sentences. It is front-loaded with the purpose, followed by the key caveat and parameter guidance. Every sentence carries meaningful information, and there is no fluff.

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?

Given the tool's simplicity (2 parameters, no output schema, no annotations), the description is quite complete. It explains what it does, the limitation that Apple doesn't expose an API, the return type (steps + deep link), and a special parameter case. It could mention what happens when data_not_collected is false, but overall it provides sufficient context for an AI agent to use it correctly.

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%, providing baseline semantics for both parameters. The description adds meaning by explaining the 'data_not_collected:true' path and implying that app_id is used to generate the deep link. This enriches the schema definitions, earning a score above baseline.

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 opens with 'Configure the App Privacy nutrition label,' which is a specific verb+resource that clearly distinguishes this tool from other 'set_' sibling tools. It unambiguously states the tool's function without ambiguity.

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 provides clear context: it's for configuring privacy labels, and the caveat 'Apple does not expose this via API; returns the exact steps + deep link' tells the user what to expect. It also gives a specific usage example ('Pass data_not_collected:true for the 'Data Not Collected' path'). However, it does not explicitly contrast with alternatives or state when not to use it, though no sibling tool seems to handle this function.

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