io.github.enzoemir1/adops-mcp

Create a realistic cross-platform ad portfolio for trying AdOps without real data. Seeds 2 connections (Google + Meta), 8 campaigns across performance tiers, 30 days of daily metrics (240 rows), and pre-computed anomaly alerts. Use this to explore what AdOps can do, then switch to `ad_csv_import` with your real Google/Meta exports for production use. Safe to call multiple times — appends a new portfolio each call.

Import real campaign + performance data from a Google Ads or Meta Ads CSV export. After import, every AdOps tool (ads_report, budget_analyze, anomaly_detect, ab_test_analyze, competitor_benchmark, forecast_spend) operates on your real data. No API keys or OAuth required — just export from your ad dashboard and import here.\n\nHOW TO EXPORT:\n • Google Ads: Reports → Campaign performance → Download → Comma-separated (.csv). Include "Day" segment for daily metrics.\n • Meta Ads Manager: Campaigns view → Export → Campaign performance (.csv). Check "Include breakdowns: By day" for daily metrics.\n\nINPUT: Provide either csv_path (absolute path to file on disk) OR csv_content (raw CSV string). Optional connection_name groups imports; re-importing with the same name appends new data. Returns a summary with campaigns_imported, metrics_imported, warnings, and the connection id to reference in other tools.

Export budget_analyze recommendations as a Google Ads Editor CSV, Meta Ads Manager CSV, JSON, or Markdown — so you can take action in one bulk paste instead of editing campaigns one by one. AdOps does not call Google/Meta write APIs (that would require OAuth + developer token approval for every user); instead, this tool hands you the exact CSV rows those dashboards expect, and you paste them in. Perfect for the "pause anything with CPA over 50 and reallocate budget" workflow. Use platform=google with format=google_ads_csv (import into Google Ads Editor). Use platform=meta with format=meta_ads_csv (bulk edit in Meta Ads Manager Power Editor). Use json to pipe into n8n or your own automation. Use markdown for human review before acting. min_delta_pct filters out noise (e.g. 0.15 = only show changes >=15%).

Register a Google Ads or Meta Ads account in the AdOps workspace so subsequent tools (campaign_list, campaign_create, ads_report) can target it. Input: platform ("google_ads"|"meta_ads"), name (display label), account_id (the external ad account id). Returns the stored connection object with a generated UUID and status="active". Safe to call with the same platform+account_id — returns the existing connection instead of erroring (idempotent).

Unified listing of campaigns across Google Ads and Meta Ads in a single view. Optional filters: platform ("google_ads"|"meta_ads"), status ("draft"|"active"|"paused"|"ended"|"archived"), query (free-text over campaign name). Pagination via limit (default 20, max 100) and offset. Returns {total, showing, offset, campaigns[]} where each campaign summary includes id, name, platform, status, objective, daily_budget, currency, and start_date. Use the returned id with campaign_update, campaign_pause_resume, or ab_test_analyze.

Create a new ad campaign in the AdOps workspace. Accepts unified parameters (platform, name, objective, bidding_strategy, daily_budget, currency, start_date, end_date, targeting) and stores a canonical UnifiedCampaign record with status="draft". Returns the created campaign summary plus next_steps guidance. Requires an active platform connection (see platform_connect) — will auto-associate the first active connection for the chosen platform.

Patch-update an existing campaign. Pass campaign_id (UUID from campaign_list or campaign_create) plus any subset of: name, status, daily_budget, bidding_strategy, end_date. Fields you omit are left unchanged. Returns {message, updated_fields[], campaign} or an error if campaign_id is not found. Prefer campaign_pause_resume for batch status changes.

Batch-change the status of up to 50 campaigns in one call. action="pause" sets status to "paused"; action="resume" sets status to "active". Missing campaign_ids are reported in the errors array but do not fail the whole batch. Returns {action, updated, failed, campaigns[], errors?}. Use this for emergency pause during an incident or weekend shutoff.

Aggregate performance metrics across Google Ads and Meta Ads into a single unified view. Input: date_range ({start, end} as YYYY-MM-DD, defaults to the last 7 days), optional platform filter, optional campaign_ids filter, optional sort_by ("spend"|"roas"|"conversions"|"ctr"|"cpc"), and limit. Returns {period, totals (spend, impressions, clicks, conversions, revenue, ROAS, CPC, CTR), by_platform, campaigns[] (sorted per sort_by), top_performers, underperformers}. This is the entry point for most analysis workflows.

Analyze how the current ad budget is distributed and produce actionable reallocation recommendations. Input: optimization_goal ("maximize_roas"|"maximize_conversions"|"minimize_cpa") and optional platform filter. Returns {goal, current_allocation (by platform + campaign), recommendations[] (each with campaign_id, current_budget, suggested_budget, rationale, expected_impact), projected_lift}. Pair with budget_reallocate to execute the recommendations.

Transfer a dollar amount from one campaign\'s daily budget to another. Works across platforms (e.g. shift $50/day from a Google Ads search campaign to a Meta Ads retargeting campaign). Input: from_campaign_id, to_campaign_id (UUIDs, must differ), amount (positive number in campaign currency). Rejects the call if from_campaign_id === to_campaign_id or if the source campaign would go below zero. Returns the updated budgets for both campaigns.

Demographic and behavioural breakdown of the audiences served by your ads. Input: platform (optional — omit for all platforms) and optional campaign_id to scope to a single campaign. Returns {age_distribution, gender_distribution, top_geos, top_interests, device_breakdown, total_impressions, engagement_rate}. Use when refining targeting or reporting audience coverage.

Platform-specific creative requirements for ad formats. Returns the exact image dimensions, aspect ratios, video duration and codec, headline/primary-text character limits, supported CTA buttons, and file size ceilings for each ad format. Input: platform ("google_ads"|"meta_ads") and optional format filter (e.g. "responsive_display", "video", "carousel", "single_image"). Use this before building creatives to avoid rejection at upload time.

Scan campaigns for statistical anomalies vs. a rolling baseline. Flags CPC spikes, CTR drops, sudden spend surges, and conversion cliffs. Input: sensitivity ("low"|"medium"|"high" — controls the z-score threshold), lookback_days (baseline window, default 14), optional platform filter. Returns {anomalies_found, severity_breakdown (critical|high|medium|low counts), alerts[] (each with campaign_id, metric, baseline, current, deviation, severity, reason)}. Run daily to catch issues before they burn budget.

Compare two campaigns as A/B test variants and determine statistical significance. Input: campaign_id_a, campaign_id_b, primary_metric ("ctr"|"conversion_rate"|"roas"|"cpc"|"cpa"). Runs a two-proportion z-test (or means comparison for continuous metrics), computes p-value and 95% confidence interval, identifies the winner, and returns {winner, confidence_level, p_value, lift_percent, sample_size_a, sample_size_b, significant (bool), recommendation}. Use with lift ≥5% and p<0.05 as a decision rule.

Compare your ad performance against industry averages for a chosen vertical. Input: industry (e.g. "ecommerce", "saas", "finance", "healthcare", "education", "travel", "real_estate", "legal"), optional platform filter. Returns {industry, your_metrics, benchmarks (CTR, CPC, CPM, conversion_rate, CPA, ROAS industry averages), comparison (percent above/below benchmark per metric), recommendations[]}. Benchmarks are curated static tables — not live market data.

Project future ad spend and performance based on recent historical trends. Input: period_days ("7"|"14"|"30") and optional platform filter. Uses moving-average extrapolation of spend, impressions, clicks, conversions, and revenue across the last 14 days. Returns {period_days, platform, projected (spend, impressions, clicks, conversions, revenue, ROAS, CPC, CTR), confidence_level, warnings[]}. Confidence drops when recent data is volatile or campaigns were paused.