meta-ads-audit

Meta Ads Audit

Diagnose Meta (Facebook + Instagram) account health and persist business context for downstream skills (/meta-ads). Read-only — never mutates the account. The user runs /meta-ads to execute fixes you recommend.

Setup

Follow ../shared/preamble.md — MCP detection, OAuth, ad account selection.

Filesystem contract (MUST persist)

| Artifact | Path | When | |---|---|---| | Business context | {data_dir}/meta/business-context.json | First full audit, or refresh when audit_date is >90 days old. Skip on scoped audits if file is fresh. | | Personas | {data_dir}/meta/personas/{accountId}.json | Every full audit. |

These are the handoff to /meta-ads — write them even if the report itself is short. Otherwise downstream skills operate without business context and produce generic output.

If a {data_dir}/business-context.json exists from /google-ads-audit (no meta/ subdir), read it as a starting point — most fields (services, brand voice, differentiators, locations, seasonality) are platform-agnostic. Then write the Meta-specific version to {data_dir}/meta/business-context.json with any Meta-specific overrides (different creative angles, different audiences, different funnel events).

business-context.json schema (shared with Google Ads where fields apply): business_name, industry, website, services[], locations[], target_audience, brand_voice{tone, words_to_use[], words_to_avoid[]}, differentiators[], competitors[], seasonality{peak_months[], slow_months[], seasonal_hooks[]}, social_proof[], offers_or_promotions[], landing_pages{}, unit_economics{aov_usd, profit_margin, ltv_usd, source}, notes, audit_date, account_id.

Meta-specific extensions: meta_funnel_events{top_of_funnel, mid_of_funnel, conversion}, creative_inventory{concepts[], formats[], aspect_ratios[]}, custom_audiences{purchasers, abandoners, engagers, list_uploads[]}, pixel_health{pixel_id, capi_enabled, emq_score, last_event_at}.

personas JSON schema: {account_id, saved_at, personas: [{name, demographics, primary_goal, pain_points[], decision_trigger, value, meta_creative_angles[], visual_cues[]}]}. The Meta version adds meta_creative_angles (e.g. "before/after demonstration", "founder-led explainer", "UGC review") and visual_cues (objects, settings, emotions that resonate with this persona). See references/persona-discovery.md.

Policy freshness check (run first)

Read ../shared/policy-registry.json. For each entry where last_verified + stale_after_days < today:

• High-volatility → WebSearch the area for recent Meta Ads changes; compare to assumption. If drift, banner the report and suggest registry update.
• Moderate-volatility → one-line "may warrant a check" note.
• Stable → skip silently.

The Meta platform changes faster than Google Ads (Advantage+, attribution, learning behaviors) — check high-volatility entries every audit.

Phase 1 — Pull the audit dataset

Use a single runScript call with ads.graphParallel to fan out the queries an audit needs. Build the fan-out from this rubric.

A complete audit needs at minimum:

• Ad account info (/{accountId}) — currency, timezone, business id, spend cap, account status, balance.
• Pixel health (/{accountId}/customconversions + /{accountId}/adspixels) — pixel id, last activity, CAPI status, Event Match Quality (EMQ) score.
• Campaigns (/{accountId}/campaigns) — id, name, objective, status, daily/lifetime budget, special_ad_categories, buying_type, bid_strategy, created_time. Last 90 days.
• Ad sets (/{accountId}/adsets) — id, name, status, campaign_id, optimization_goal, billing_event, bid_strategy, daily_budget, lifetime_budget, attribution_spec, targeting (summary), promoted_object, learning_stage_info.
• Ads (/{accountId}/ads) — id, name, status, ad set, creative summary (image/video, primary text, headline, description, CTA), effective_status.
• Insights at campaign level (ads.insights({level:"campaign", date_preset:"last_30d"})) — spend, impressions, reach, frequency, cpm, link CTR, link clicks, purchases (or other primary action), purchase value, ROAS, CPA.
• Insights at ad set level — same fields, last 30 days.
• Insights at ad level — top 50 ads by spend; same fields plus video metrics (3-sec views, ThruPlays) for video creatives.
• Insights with breakdowns — placement (publisher_platform,platform_position), age/gender, device. Use these to spot placement losers and audience composition.
• Recent edit activity — when available via /{adsetId} last_modified or /{adsetId} change history.

Compute aggregates in the script, return summarized JSON. Don't return all rows — rank, slice, summarize. The agent narrates the result; the script does the math.

suggestImprovement is a useful cross-check for the server's heuristic surface — call it as a separate tool after the runScript pass if you want to compare your findings.

If a critical query errors out (auth, schema, API version), surface the error and stop — don't fall back to a degraded audit.

Skip scoring entirely if totalSpend == 0 or activeCampaigns == 0. Go straight to business context.

Phase 2 — Scope handling

If the user narrows the audit ("focus on one campaign", "campaign X", "just check creative fatigue"):

• Match campaign names by case-insensitive substring. If no match, list available campaigns and ask.
• Filter the in-memory dataset before scoring — no extra API calls.
• Account-level dimensions (Pixel health, attribution defaults) stay account-wide. Note "Scoped to: X" in the report.
• Skip Phase 4 (business context refresh) on scoped audits if business-context.json is fresh.

Phase 3 — Score

Score each of the 7 dimensions 0–5 using references/account-health-scoring.md. Overall = round(sum × 100 / 35).

| Score | Label | Meaning | |---|---|---| | 0 | Critical | Broken or missing — actively losing money | | 1 | Poor | Major waste or missed opportunity | | 2 | Needs Work | Several clear issues | | 3 | Acceptable | Functional, room to improve | | 4 | Good | Well-managed, minor opportunities | | 5 | Excellent | Best-practice |

Scope-aware: campaign-level dimensions reflect in-scope data; account-level dimensions (Pixel + CAPI, attribution setup) score account-wide with a note on scope impact.

Encoded heuristics — apply these, they aren't obvious

• Pixel + CAPI is upstream of everything. EMQ < 7.0 means Meta can't match events well — Smart Bidding starves regardless of how good the creative is. STOP-condition input.
• Reported ROAS systematically overstates true ROAS. Cross-check Meta-reported numbers against Shopify / GA4 / MMM where possible. The gap is the modeled-conversion premium and is typically 20–40% in ecom.
• Frequency × CPM trend = creative diagnosis. Frequency > 3.0 with CPM rising ≥ 30% w/w is fatigue — recommend creative refresh, not budget cuts.
• One ad set carrying > 70% of a campaign is fragility, not concentration. When it fatigues, the campaign collapses.
• Audience overlap > 50% between sibling ad sets fragments signal. Consolidate; don't try to "fix" with bid caps.
• Special Ad Category misclassification is a takedown risk, not just a policy nit. Surface as Critical regardless of current performance.
• Manual placements without evidence is a sign of inherited-from-2018 thinking. Default should be Advantage+ Placements; deviations need data.

Pixel + Tracking Diagnosis Matrix

| | EMQ < 5 | EMQ 5–6.9 | EMQ 7.0+ | |---|---|---|---| | CAPI off | Critical — flying blind | Critical — most events lost | High — leaving 15–25% of events on the table | | CAPI on, dedup off | Critical — duplicated and weak signal | High — duplicate counting risk | Medium — match quality improves with dedup | | CAPI on, dedup on | High — match quality is the bottleneck | Medium — improve event_id coverage | Healthy |

Phase 4 — Business context