AI Visibility Checker

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds value by disclosing the specific checks performed and the return format (score with gaps), and mentions 'Free.' which is additional behavioral context beyond 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 purpose and key details. Every sentence is necessary: first states action and scope, second specifies output and cost. No superfluous content.

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 one simple parameter, no output schema, and annotations present, the description covers the tool's input, action, and output adequately. It lacks details on rate limits or prerequisites, but the tool's simplicity makes this acceptable.

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?

Only one parameter (url) with 100% schema description coverage. The description merely restates the parameter's purpose ('Website URL to audit'), adding no semantic nuance beyond the schema. Baseline 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 clearly states the tool audits website readiness for AI agents, listing specific checks (crawler access, llms.txt, schema.org data, .well-known manifest) and output (0-100 score with gaps). This distinguishes it from the sibling check_ai_visibility, which likely covers a different aspect.

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 explicitly states what the tool does and that it's free, implying usage when assessing AI agent readiness. However, it does not provide explicit when-to-use or when-not-to-use guidance relative to the sibling tool, which would elevate the score further.

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.