ai-visibility-check

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

With no annotations provided, the description carries the full disclosure burden. It transparently states that a live AI search (Perplexity) is run, citations are checked for rank, competitors are identified, and outputs include a score, tier, per-query results, and a scorecard link. Minor gaps such as rate limits, cost, or failure behavior are not mentioned, but the core behavior is clear.

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 focused paragraph with no redundant content. The first sentence states the purpose, followed by the execution method, query constraints, and output summary. Every sentence earns its place and the most important scoping details are front-loaded.

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 4-parameter tool with full schema coverage and a declared output schema, the description covers the why, how, and what-is-returned. It gives enough context for query selection and expected results, though it does not address error cases or what happens when a query returns no citations. That is a minor omission given the schema and output description.

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 baseline is 3. The description adds value by explaining the intent behind the queries parameter ('realistic customer-intent questions — what a potential customer would actually ask an AI') and supplying a concrete example. It does not add meaning for domain, business_name, or email beyond what the schema already 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?

The description opens with a specific verb and resource: 'Check whether a business is visible to AI answer engines.' It then details the mechanism (live AI search via Perplexity) and the exact report contents, leaving no ambiguity about what this tool does.

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?

There are no sibling tools to contrast, but the description gives clear invocation guidance: pass 1-5 realistic customer-intent questions, never include the business name, and includes a concrete example. It implies the use case (checking AI visibility) but does not explicitly say when to avoid using it, though no alternatives exist.

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