AEO Audit

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

Annotations already declare readOnlyHint and openWorldHint, so the description adds value by specifying that it checks how a domain appears across AI answer engines and returns specific metrics. No contradictory or missing behavioral traits.

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 three concise sentences, each adding value: the main action, what is checked, and what is returned. No unnecessary words, and the structure is 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?

Given there is no output schema, the description adequately covers return values (citation rate, grade, etc.) and the scope of the audit. However, it does not explain the grading scale or what 'full' entails, leaving minor gaps.

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%, so parameters are well-documented in the schema. The description adds minimal new insight, only implicitly referencing competitors through 'competitor comparison' in the output.

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 it runs a full AEO audit for a domain, listing specific outputs like citation rate, grade, competitor comparison, and per-query results. This distinguishes it from siblings like quick_check (likely lighter) and audit_domain_premium (premium version).

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 explains what the tool does but does not provide explicit guidance on when to use it versus siblings like audit_domain_premium or quick_check. The usage context is implied but not stated, leaving the agent to infer.

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 provide readOnlyHint and openWorldHint. The description adds significant behavioral context: pricing model (subscription or per-call) and the detailed output structure (recommendations, implementation guide, competitive analysis, score improvement). This goes beyond annotations to help the agent understand the tool's cost and output richness. However, it doesn't mention rate limits or authentication beyond the payment link.

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 with a clear opening statement, followed by pricing and a bullet list of included features. Every sentence adds value, though the pricing details could be seen as secondary. The structure is front-loaded and easy to parse.

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's complexity (premium audit with multiple deliverables) and the lack of an output schema, the description adequately lists what the tool returns (recommendations, guide, competitive analysis, score estimates). It does not specify the return format (e.g., JSON or text), but for a complex tool the level of detail is sufficient.

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 baseline is 3. The description does not add any information about parameters, their formats, or relationships. It focuses on the tool's purpose and outputs rather than parameter usage. No extra value beyond the schema.

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 it as a 'Premium AEO audit' and indicates it includes everything from the sibling tool 'audit_domain' plus additional features. The purpose is clear, though it could be more explicit about the action (e.g., 'Perform a premium audit'). It effectively distinguishes itself from siblings by specifying the premium nature.

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 by comparing to 'audit_domain' (basic vs premium), but does not explicitly state when to use this tool versus alternatives like 'quick_check' or 'suggest_queries'. No clear when-not or alternative guidance beyond the cost hint. Context is present but not fully explicit.

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 (safe read) and openWorldHint=true (external data, potentially inconsistent). The description adds 'quick single-query AI visibility check' but does not elaborate on behavior beyond what annotations provide, such as whether results are cached, rate-limited, or how the check is performed. Minimal added value.

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, short and to the point. It front-loads the purpose effectively. However, it could be slightly expanded to include key behavioral notes without becoming verbose.

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 low complexity (no output schema, 3 parameters with 67% coverage, no nested objects), the description provides the basic purpose. However, it lacks any mention of return values or output format, which is a notable gap for a tool that likely returns a result.

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 67%, with descriptions for 'query' and 'domain' but not for 'provider' (enum). The description does not add any parameter-specific meaning, such as explaining the 'provider' parameter's role or valid values, leaving the agent to infer from the enum alone.

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 'Quick single-query AI visibility check for a domain' clearly indicates the tool's verb ('check') and resource ('AI visibility' for a domain). It distinguishes from sibling tools (audit_domain, audit_domain_premium, suggest_queries) by implying a quick, singular query focus, though it could be more explicit about what 'visibility' entails.

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 for quick checks via the term 'quick single-query', but provides no explicit guidance on when to use this tool vs. alternatives, no when-not-to-use conditions, and no mention of prerequisites or 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 idempotentHint, so description doesn't need to repeat. The word 'generate' might imply creation, but in context it means returning suggested queries. No additional behavioral details provided.

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?

Single sentence is concise, but lacks sufficient detail to be truly helpful; it is neither verbose nor wasteful.

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 4 parameters and no output schema, the description is too sparse. It does not explain what sort of queries are returned, how count affects output, or the role of domain vs industry.

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 0%. The description does not explain any of the 4 parameters (count, domain, industry, language), leaving the agent without guidance on how to fill them meaningfully.

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 it generates AEO audit queries for a domain/industry, but does not differentiate from sibling tools like audit_domain or audit_domain_premium, which might perform actual audits.

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?

No guidance on when to use this tool versus alternatives; the description does not explain that it is for query suggestions versus running audits or that it is safe/read-only.

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