AI Visibility Checker

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

Annotations already mark the tool as read-only and non-destructive; the description adds useful behavioral context beyond that by stating it returns a 0-100 score with gaps, is free, and transparently discloses an upsell to a paid fix pack. This pricing and commercial behavior is beneficial for the agent to know.

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 opening sentence is dense but informative, and the return-score statement is concise. However, the promotional sentence about the $99 BuyReady pack with a long URL adds marketing noise and is not essential for tool selection, making the description less economical than it could be.

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 simple one-parameter, read-only tool with no output schema, the description is complete: it explains what is audited, what criteria are checked, what output shape to expect (0-100 score with gaps), and even flags cost/pricing behavior. No critical operational context appears missing.

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 schema already fully describes the only parameter (url) with 100% coverage, so the description does not need to add much. It refers to auditing a domain and the paid deliverable, but it does not add new parameter syntax or format guidance beyond what the schema provides.

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 a specific action ('Audit whether a website is ready') against a defined resource (a website for AI agents), naming concrete audit components: AI-crawler access, llms.txt, schema.org data, and agent manifest. It also defines the output as a 0-100 readiness score with gaps, which differentiates it from the sibling check_ai_visibility.

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 establishes clear use context: use this when you need to audit a website's AI-agent readiness and get a numeric score with identified gaps. It does not explicitly name when to prefer this over check_ai_visibility, but the audit-specific framing makes the intended use clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, and destructiveHint false. The description adds valuable context: it specifies the output (score 0-100, frequency, competitor names), notes it uses a representative LLM and real buyer questions, and states it is free. No contradiction with annotations.

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, front-loaded with the main purpose, and every clause adds meaningful information. It efficiently conveys tool function, outputs, model type, and cost without waste.

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 output schema exists and the annotations cover safety, the description is complete for this tool's complexity. It explains what the tool does, what it returns, and the representative nature of the model, leaving little ambiguity for an agent.

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 input schema covers 100% of the parameters, so the description does not need to add parameter detail. It does not discuss parameters at all, but the schema already provides adequate descriptions, yielding the baseline score.

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 a specific verb ('Estimate') and resource ('brand recommendation visibility') with a precise scenario (when buyers ask an LLM for recommendations in its category). This distinguishes it from the sibling tool check_agent_readiness, which presumably addresses a different question.

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 the usage context clearly: use this tool to estimate how visible a brand is in AI-generated recommendations. However, it does not explicitly state when not to use it or compare it with alternatives, so it falls short of a full 5.

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