SEO Audit

What this measures

How well a page can be crawled, chunked, understood, and cited by AI answer engines — not classic blue-link SEO. The audit_page tool computes this deterministically: page data flows through context.dev, then a fixed rubric of ~30 checks scores six categories into a 0–100 total. Your job is to present that result, not to re-score it.

The result shape

audit_page returns JSON with:

• score (0–100) and band ({ label, interpretation }).
• stats — words, headings, links, externalLinks, jsonLdBlocks, char counts.
• categories[] — each { id, name, score, maxScore, naExcluded, items[] }.
• categories[].items[] — each check: { id, label, status, evidence, recommendation, score, maxScore }. status is pass | partial | fail | na.
• topPriorities[] — the highest-impact failing/partial checks, pre-sorted.
• agentPrompts — { full, topIssues[] }, ready-to-run fix prompts.
• diagnostics — context.dev fetch status and JSON-LD parse failures.

The six categories

Scoring (already applied by the tool): pass = full weight, partial = half, fail = 0, na = excluded from the denominator. Bands: Critical ≤40 · Below average ≤55 · Average ≤70 · Strong ≤85 · Excellent >85.

Output

Report, in order:

1. The overall score/100, its band.label, and the band.interpretation.
2. A per-category breakdown: for each category, score/maxScore and the pass/partial/fail counts.
3. The failing and partial checks, highest-impact first — use topPriorities and the matching items (cite each check's id, evidence, and recommendation).
4. The agent-ready fix prompt: present agentPrompts.full verbatim in a copy-paste block.

Report only the checks and numbers the tool returned. Never invent or recompute scores, checks, evidence, or content.