Sitelemetry

AI visibility (GEO/AEO) audit

audit_ai_visibility

Assess how visible and citable a site is to AI search and LLMs (ChatGPT/Claude/Perplexity/Google AI): entity clarity, answerability, prompt coverage, source signals, llms.txt/AI-crawler policy. Returns findings with fixes.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`lang`	No	Report language (default en): en, tr, es, de, fr, pt, it, ja, zh.
`target`	Yes	Target website URL or domain you own / are authorized to audit (e.g. https://example.com).

Output Schema

TableJSON Schema

Name	Required	Description
`kind`	Yes	Audit pillar represented by this result.
`grade`	Yes	Human-readable grade, or null when unavailable.
`score`	Yes	Measured score from 0 to 100, or null when unavailable.
`total`	Yes	Total findings produced before response truncation.
`counts`	Yes	Finding totals grouped by normalized severity.
`target`	Yes	Canonical audited target.
`findings`	Yes	Prioritized findings with evidence, impact, and remediation.
`passingChecks`	Yes	Number of checks that passed or reported protection.
`returnedFindings`	Yes	Number of findings included in this response.
`findingsTruncated`	Yes	True when additional findings exist outside this response.

TDQS

A4.3/5.0

Behavior4/5

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

Beyond the annotations, the description adds behavioral context: the audit examines defined AI signals and returns findings with fixes. The target parameter also adds an important authorization constraint, and the read-only/destructive profile is already covered by annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact, front-loaded with the core purpose, and uses a tight list for audit dimensions without filler. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The definition covers what is audited, what results to expect, the authorization requirement, and optional report language. Since an output schema exists, the single mention of 'Returns findings with fixes' is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so both target and lang are already clearly documented with defaults, enums, and examples. The description does not need to add parameter-specific detail because sender the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('Assess how visible and citable a site is to AI search and LLMs') and names concrete dimensions it covers, such as entity clarity, answerability, and llms.txt/AI-crawler policy. This makes it immediately distinguishable from sibling audit tools like audit_seo and audit_accessibility.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The intended use case is clear: run this when auditing a site's AI search and LLM visibility in a GEO/AEO context. It does not explicitly name exclusions or alternative audit tools, but the AI/LLM framing is explicit enough to guide selection.

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

All Sitelemetry tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.3/5.0

Disambiguation5/5

Each audit tool targets a distinct dimension of site health (accessibility, AI visibility, integrations, performance, security, SEO), with audit_full explicitly composing them. The boundaries are clearly described, so there is no realistic ambiguity in choosing which tool to invoke.

Naming Consistency5/5

All seven tools follow the exact same verb_noun snake_case pattern with audit_ as a prefix, followed by a descriptive domain element. This creates an entirely predictable and consistent naming convention.

Tool Count5/5

Seven tools is a well-scoped count for a site auditing server: six specialized audits plus one composite full audit. No tool feels redundant, and the set is neither too thin nor too heavy for its stated purpose.

Completeness5/5

The tool surface covers all major established audit domains—accessibility, AI visibility, integrations/unwanted trackers, SEO, performance, and security. The full audit ties everything together with summaries and fixes, creating a complete audit-fix-retest lifecycle without obvious gaps.

Resources

Need Help?