Analyze AI visibility

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

Annotations already declare readOnlyHint=false, openWorldHint=true, destructiveHint=false, and the description is consistent with them while adding real value: it flags the limited/non-representative dataset, the credit/BYOK requirement, the mandatory cost cap, and the write-scope auth need. It stops short of describing return format, pagination, or exactly how credits are spent, which keeps it from a 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 tight sentences with the operation front-loaded and the constraints following. No filler, though the prerequisites sentence could be marginally tighter.

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 12-parameter paid-read tool with a fully documented schema and no output schema, the description covers the essential context: provider scope, dataset limitation, and credit/auth requirements. It leaves out how rows are returned or bounded, but the schema already covers limit/estimate_only, so this is nearly complete.

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 carries full parameter semantics and the baseline of 3 applies. The description adds only a passing reference to the two provider datasets and does not clarify the cost-cap or idempotency semantics beyond what the schema states.

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?

States a specific verb ("Inspect") and resource ("provider-observed ChatGPT or Google AI Overview mentions and citations"), so the core operation is unambiguous. However, it offers no differentiation from the large AI-tracking family of siblings (compare_ai_prompts, get_ai_tracking_trends, list_ai_tracking_samples, export_ai_tracking_evidence), so an agent cannot tell from the description alone which one to pick.

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 discloses prerequisites (BYOK or deployment credits, explicit cost cap, project write scope) and a scope caveat (limited provider dataset, not all conversations), which is useful implied context. But it never states when to choose this over the sibling AI-tracking tools or when not to use it, leaving the selection decision to inference.

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