Sitelemetry

Performance & Core Web Vitals audit

audit_performance

Measure performance via Google PageSpeed Insights (Lighthouse lab + CrUX field data): LCP, INP, CLS, FCP, TTFB, and the top improvement opportunities. Returns findings with fixes. (Uses the operator PAGESPEED_API_KEY when set.)

Input Schema

TableJSON Schema

Name	Required	Description
`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).
`strategy`	No	Device strategy (default mobile).

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?

The description adds useful behavioral context beyond the annotations: it uses Google PageSpeed Insights, combines Lighthouse lab data with CrUX field data, returns improvement opportunities with fixes, and uses the operator PAGESPEED_API_KEY when set. No statement contradicts the annotations, though it does not discuss failure modes like missing keys or rate limits.

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 and front-loaded. It states the action, names the metrics, summarizes the output, and records the key environment variable note in just a few sentences with no padding.

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?

Given the complete input schema, output schema, and existing annotation hints, the description provides sufficient context for an agent to select and invoke this tool. It covers the essential external dependency, the metric scope, and the expected result format.

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?

The input schema already has 100% parameter coverage, including descriptions for target, lang, and strategy. The tool description adds general context but no new per-parameter meaning beyond what the schema provides, 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 clearly states the action: measure performance via Google PageSpeed Insights, and it names the specific metrics (LCP, INP, CLS, FCP, TTFB). This makes it easy to distinguish from the sibling audit tools for accessibility, AI visibility, SEO, security, and integrations.

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 obvious from the title and metric list: this is the performance/Core Web Vitals audit. It does not explicitly name sibling alternatives or exclusion criteria, such as when to use audit_full instead, so it stops short of full routing 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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