get_ai_visibility

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does this well by explaining that results are per-engine, never averaged, and that the answer is not deterministic ('a model's knowledge is frozen at a date and the answer is not identical every time'). This is valuable behavioral context beyond what the schema shows. It could go further by describing the output format, but the key behavioral traits are disclosed.

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 two sentences with zero waste. The core purpose is front-loaded, and the important caveat about observation vs. ranking is included without bloat. Every sentence earns its place.

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 tool with 2 parameters, 100% schema coverage, and no output schema, the description is quite complete. It explains the purpose, the per-engine reporting, and the non-deterministic nature. The only gap is that it doesn't describe the return format, but since there's no output schema, a brief note on what the response looks like would be helpful. Still, the description covers the essential context an agent needs to call 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 description coverage is 100%, so the schema already documents both parameters (appId and country). The description doesn't add much parameter-level detail beyond what the schema provides, but it does imply that appId is the primary input. Baseline 3 is appropriate since the schema does the heavy lifting.

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 purpose: it reports whether AI assistants name this app when asked natural questions about its category, per engine. It distinguishes itself from a ranking by explicitly saying 'This is an OBSERVATION, not a ranking.' This is a specific verb+resource with clear scope, and it differentiates from sibling tools like get_charts or get_reviews.

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 on when to use this tool: when you want to know AI assistants' visibility for an app, reported per engine. It also gives guidance on how to interpret results ('read the trend rather than a single measurement'). However, it doesn't explicitly name alternative tools or state when NOT to use it, so it doesn't fully reach a 5.

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