get_ai_visibility

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

Annotations already cover the safe-read profile (readOnly, idempotent, non-destructive), and the description goes well beyond them: it names the required plan and scope, explains the `engines`/`providers` return structure, and crucially clarifies that measured/failed/not_measured states exist and 'an unavailable engine is not a zero score.' That interpretation guidance is exactly the kind of context annotations cannot convey.

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 purpose is front-loaded, but the text is dense and carries overlapping disclaimers ('does not reproduce what ChatGPT... show each user' vs 'not a universal ranking or a measure of all consumer answers') that repeat the same caveat. The result-structure sentence sits awkwardly amid the caveats.

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 two-parameter, well-annotated read tool with no output schema, the description covers the essentials: return keys, engine-state semantics, auth requirements, and a key interpretation warning. Only the distinction from the sibling check tool and pagination/sample-size behavior are unaddressed.

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 appId and country are already documented in the schema. The description adds no syntax, format, or constraint detail about either parameter, so the 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?

The description states a specific verb+resource: app mentions drawn from a dated sample of AI engine answers, and explicitly scopes what it is not (not a per-user reproduction of ChatGPT/Claude/Gemini output). This is clear, though it never names the closest sibling run_ai_visibility_check, so the boundary between reading results and running a check is left implicit.

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?

It supplies a real prerequisite (Studio or Enterprise plan plus the ai:read scope) and warns that the output is not a universal ranking. However, it offers no explicit when-to-use / when-not-to-use versus alternatives such as run_ai_visibility_check, leaving selection largely to inference.

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