Sitelemetry

Analytics & tracking audit

audit_integrations

Detect and audit the analytics/tag/marketing-pixel/verification/consent stack from the site HTML + headers: GA4, GTM, Meta Pixel, TikTok/LinkedIn pixels, Search Console/Bing verification, Hotjar/Clarity, consent/CMP, and PII-in-dataLayer leaks. Returns gaps + 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

A4.1/5.0

Behavior4/5

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

With annotations already covering safety (destructiveHint=false) and open-world behavior, the description adds useful behavioral context: the audit is derived from site HTML and headers, and it returns gaps plus fixes. It does not go into rate limits or authorization caveats, but the parameter description already flags authorized targets, so this is reasonably transparent.

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

Conciseness4/5

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

The description is a single dense sentence with the main purpose at the front and a compact 'Returns gaps + fixes' at the end. It is longer than strictly necessary, but every part contributes meaningful scope detail.

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

Completeness4/5

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

With full parameter descriptions, output schema present, and annotations exposing safety behavior, the description is sufficient for an agent to understand what the tool covers and what it returns. It does not explicitly mention limitations, such as only client-side detection, but that is a minor gap.

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?

Input schema coverage is 100%: target and lang are both described with clear meaning, and lang has an explicit enum. The main description adds little parameter-level detail beyond the schema, so the baseline score of 3 is appropriate.

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 verb and resource: it detects and audits the analytics, tag, marketing-pixel, verification, and consent stack from the site HTML and headers. The explicit list of technologies (GA4, GTM, Meta Pixel, etc.) clearly distinguishes it from the other audit_* sibling tools.

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 description gives clear context about the tool's specialty: analytics, tags, pixels, consent/CMP, and dataLayer leaks. It does not explicitly name sibling tools or say 'use this instead of audit_security,' but the tightly scoped wording makes the intended use apparent. Slight deduction because there is no explicit when-not-to-use guidance.

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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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.

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