AEO Audit

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

Annotations already declare read-only and open-world behavior; the description adds meaningful context by stating that it checks appearances across AI answer engines and returns citation rate, grade (A-F), competitor comparison, and per-query results. It does not contradict the annotations and provides more detail than the annotations alone.

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 with no filler: the first sentence delivers the core action and scope, the second summarizes the key outputs. It is compact, front-loaded, and every clause earns its place.

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?

With no output schema, the description appropriately lists the main return categories (citation rate, grade, competitor comparison, per-query results) and the audit scope. It doesn't mention the default provider or query limits, but the input schema covers those details, so the description is sufficiently complete for a tool of this complexity.

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 coverage is 100% and each parameter already has a clear description. The tool description uses 'domain,' 'given queries,' and 'competitor comparison' but adds little beyond the schema's own parameter descriptions, so it stays at the baseline for high schema coverage.

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 ('Run a full AEO audit for a domain') and goes on to list concrete outputs (citation rate, grade, competitor comparison, per-query results). The word 'full' helps distinguish it from quick_check and suggests a comprehensive audit.

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 a comprehensive audit use case via 'full', but it never explicitly states when to use this tool versus audit_domain_premium or quick_check. There are no exclusions or direct alternative recommendations, leaving the agent to infer usage from sibling names and context.

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 and openWorldHint, so the description focuses on additional behavioral context: the tool requires payment via subscription or per-call USDC, and it references external services (Stripe, x402). This adds valuable context about costs and external dependencies.

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 concise and front-loaded with the core purpose, followed by pricing and feature list. The bullet points improve scannability, and there is no redundant information.

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 absence of an output schema, the description adequately informs the user about what to expect in the output (recommendations, guide, analysis, score improvement). It also references the base audit_domain tool to inherit its behavior, providing sufficient context for a premium variant.

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 covers all parameters with descriptions, and the description does not add parameter-specific syntax or format details. However, it does imply the 'competitors' parameter is used for competitive gap analysis, which is a subtle enhancement.

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 identifies this as a 'Premium AEO audit' and lists specific deliverables (recommendations, implementation guide, competitive gap analysis). It distinguishes from the sibling audit_domain by stating 'Includes everything in audit_domain PLUS', making the scope unambiguous.

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 when the user needs premium features beyond the basic audit, explicitly listing the added value. It also provides pricing and payment options, but does not explicitly state when not to use this tool or alternatives like quick_check.

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 and openWorldHint. The description adds the 'single-query' scope, which is useful, but it does not disclose return format, provider behavior, or other behavioral details. No contradictions 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?

One sentence of seven words, front-loaded with the core purpose. Every word earns its place, and there is no redundant phrasing or unnecessary detail.

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 3-param tool with no output schema, the description is minimally sufficient but leaves gaps: it doesn't describe the response format, provider selection implications, or how this differs from audit_domain. It is adequate but not comprehensive.

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 descriptions cover domain and query, while provider has only enum values with no description. The tool description does not mention provider or explain defaults, so it doesn't compensate for the missing provider description. However, the enum values (exa, perplexity) are self-explanatory.

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: 'Quick single-query AI visibility check for a domain.' This distinguishes it from siblings like audit_domain (comprehensive audit) and suggest_queries (query generation), making the purpose unambiguous.

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 'quick' and 'single-query' wording implies it is for lightweight, focused checks rather than full audits, but it does not explicitly say when to use this tool instead of audit_domain or audit_domain_premium, nor does it offer exclusions or alternative recommendations.

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=true and idempotentHint=true, so the agent knows this is safe and repeatable. The description adds minimal behavioral context beyond 'generate queries', suggesting it returns queries rather than performing an audit, but provides no details about rate limits, response format, or side effects. The description does not contradict 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 a single sentence, front-loaded with the action ('Generate AEO audit queries') and scope ('for a domain/industry'). Every word earns its place; there is no fluff or repetition. It is concise and well-structured for quick scanning.

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 has 4 parameters, no output schema, and no parameter descriptions, the description is under-specified. It does not explain what the generated queries look like, how count/language affect results, or how it differs from the sibling audit tools. The read-only annotations cover safety, but the description leaves significant gaps for an agent to invoke it 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?

With schema description coverage at 0%, the description must explain parameter meanings. It only covers 'domain/industry' implicitly, leaving 'count' and 'language' unexplained. The schema's constraints (count min/max, language enum) are not referenced, so the description adds only a partial bridge between parameters and intent.

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's function: 'Generate AEO audit queries for a domain/industry.' It uses a specific verb ('generate') and identifies the resource (AEO audit queries). It distinguishes itself from sibling tools like audit_domain and audit_domain_premium by implying it produces queries rather than conducting audits, though it doesn't explicitly name alternatives.

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 provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or scenarios where this tool is preferred over audit_domain or quick_check. The usage context is only implied by the tool's name and purpose.

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