get_ai_visibility

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

No annotations are provided, so the description carries the full burden. It clearly implies a read-only operation ('Get') and adds context about the data source (AI-generated responses across specific models). However, it does not mention limitations, authentication, output format, or whether data is real-time or historical, which are relevant behavioral details for this type of tool.

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 a single, well-structured sentence. It front-loads the verb and resource, adds useful detail about models, and contains no redundant or extraneous words.

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 low complexity (one required parameter, no output schema, no annotations), the description is mostly complete. It clearly explains the core function and what kind of data is returned. The only gap is that it does not specify the structure or fields of the output, but for a simple tool, this may be sufficient. The description provides enough context for an agent to select the tool 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?

The only parameter, project_id, is fully described in the schema ('The project ID'), achieving 100% schema coverage. The description adds no additional meaning to the parameter, so the baseline score of 3 is appropriate.

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 uses a specific verb ('Get') and resource ('AI search visibility data') and clearly explains how the feature works by mentioning brand appearance in AI-generated responses across models like ChatGPT, Claude, Gemini, and others. This clearly distinguishes it from sibling tools like get_serp_results or get_keywords, which focus on traditional SERP or keyword data.

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 implies usage by stating exactly what data the tool provides (AI visibility), so a user needing AI brand visibility would understand this is the right tool. However, it does not explicitly mention when not to use it or name any alternative tools, leaving the usage guidance implied rather than explicit.

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