Run a full AEO (Answer Engine Optimization) audit for a domain. Checks how the domain appears across AI answer engines for given queries. Returns citation rate, grade (A-F), competitor comparison, and per-query results.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | The domain to audit (e.g., 'example.com') | |
| queries | Yes | Search queries to test | |
| provider | No | AI engine (default: perplexity) | |
| competitors | No | Competitor domains |
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.
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audit_domain and audit_domain_premium are nearly identical in purpose, with premium being a superset, and quick_check also performs a similar visibility check. This creates significant ambiguity about which tool to use for a given task, despite distinct feature lists.
Tool names mix patterns: 'audit_domain', 'audit_domain_premium', 'quick_check', and 'suggest_queries' vary between verb_noun, suffixed verb_noun, adjective_noun, and verb_noun. The inconsistency makes predicting tool names or extensions harder.
With only 4 tools, the count is appropriate for a focused audit service. Each tool corresponds to a distinct workflow step or tier, and the number is well within the typical 3-15 range.
The set covers the full audit lifecycle: query generation, quick checks, full audits, and premium audits with recommendations. Minor gaps like historical audit tracking or comparison between runs exist, but core domain needs are well served.