Ask AI

{
  "id": "aix_ab05417ff0",
  "name": "Ask AI",
  "operator": "(unclaimed - source: registry-official · publisher: com.ask-ai-data-connector)",
  "description": "Ask questions across Shopify, Klaviyo, GA4 and 20+ e-commerce sources in plain English.",
  "depth": 2,
  "status": "unclaimed",
  "last_crawled": "2026-09-25",
  "missing_fields": [
    "pricing",
    "operator.identity"
  ],
  "skills": [
    "browser-automation",
    "calendar-sync",
    "dedupe-records",
    "inventory-check",
    "market-data",
    "order-tracking",
    "streaming-tools"
  ],
  "protocols": {
    "mcp": "https://ask-ai-data-connector.com/mcp",
    "a2a": null
  },
  "pricing": null,
  "regions": [
    "global"
  ],
  "languages": [
    "en"
  ],
  "reputation": {
    "tasks_completed": null,
    "dispute_rate": null,
    "p95_latency_ms": 563,
    "uptime_30d": 1,
    "onchain_volume_30d_usd": null
  },
  "aix_score": 67,
  "verification": {
    "identity": "none",
    "health": "probe/24h",
    "pricing": "unknown",
    "last_check": "2026-09-25T11:01:46.114Z"
  },
  "pricing_model": "unknown",
  "links": [
    {
      "label": "homepage",
      "url": "https://ask-ai-data-connector.com/"
    }
  ],
  "profile": {
    "mcp_server": "ask-ai-dataconnector",
    "mcp_version": "1.0.0",
    "tool_count": 66,
    "tools": [
      {
        "name": "get_revenue_drivers",
        "description": "Diagnostic tool for 'why did revenue change?' questions. Bundles headline metric deltas, product mix shifts, and pre-generated ranked hypotheses into one call — so you don't have to stitch signals together yourself and risk asserting causation. Each hypothesis includes: what signal supports it, what else would be true if it's correct, and which tool to call next to confirm or rule it out. Use when the merchant asks why revenue went up or down, what's driving performance, or what changed."
      },
      {
        "name": "get_anomalies",
        "description": "Anomaly detection across revenue, conversion rate, and refund rate for each of your stores (works for a single store too). Compares the most recent 7-day window against the prior 4 weeks and flags moves outside +/- 1.5 standard deviations or 25% — whichever is stricter. Returns ranked anomalies with store, metric, current vs baseline, severity, and a one-line explanation. High-severity anomalies carry EITHER `drivers`+`driverSummary` (the tag/source/country slice that drove the move) OR `driversNote` (attribution ran, the move is broad-based — sitewide cause, not one channel), plus an `investi"
      },
      {
        "name": "get_sync_health",
        "description": "Report the health of every connected data source for each of your stores (works for a single store too). For each provider/instance: last successful sync timestamp, record count, freshness flag (stale if > 36h), and key-field nullability rates (e.g. % of customers without firstOrderAt populated). Use this to diagnose 'why is metric X showing 0?' or to confirm data is current before reporting numbers."
      },
      {
        "name": "get_data_sources",
        "description": "CALL THIS FIRST to see which data sources are connected and have data. Returns connection status and record counts for: Shopify (always connected), Triple Whale, Klaviyo, Gorgias, Recharge, Google Search Console, Google Analytics, Microsoft Clarity, and YouTube. Use this to understand what data is available before making other queries. If a source shows 'not_connected', those tools will return empty results."
      },
      {
        "name": "get_complete_dashboard",
        "description": "Get a unified dashboard with key metrics from ALL connected data sources in one call. Returns a lean snapshot of revenue, traffic, marketing, email, organic search, UX, support, reviews, subscriptions, and loyalty — only for sources that are connected. Use this for 'Give me an overview' or 'How is the business doing?' then drill into specifics with individual tools. Note: in multi-store mode, the store param filters Shopify data (revenue, orders, products, customers) to that store. Non-Shopify sources (GA4, Klaviyo, Search Console, Gorgias, etc.) are account-level and always return aggregated "
      },
      {
        "name": "get_metrics_comparison",
        "description": "BEST FOR COMPARISONS AND THE CANONICAL CONVERSION RATE: Get core e-commerce metrics (Revenue, Sessions, CVR, AOV, Revenue per Session) with period-over-period comparisons. CVR here uses Shopify TrafficStat sessions (Plus/Advanced) — or GA4 sessions as fallback — divided by REAL Shopify orders. This is the right source for 'what's my conversion rate?' / 'how's my CVR trending?' questions. Do NOT use Triple Whale pixel CVR (from get_connector_data(connector:'triple-whale') or get_marketing_performance._pixelFunnel) as a stand-in: pixel undercounts purchases and sessions and produces misleading a"
      },
      {
        "name": "get_metrics_multi_compare",
        "description": "Get metrics with MULTIPLE comparisons at once. Perfect for questions like 'Show me revenue WoW and YoY' or 'Compare this week to last week and same week last year'. Returns { period, metrics, comparisons, charts, presentation }. `charts` is an array of grouped-bar specs (one per headline metric) with 3+ bars each (current + each comparison period). DEFAULT TO RENDERING THE RELEVANT CHART for multi-period questions — bars side-by-side communicate the deltas instantly. Pull each comparison's % change from its `changes` object for captions."
      },
      {
        "name": "get_yoy_monthly",
        "description": "Month-by-month year-over-year comparison for a single metric. SINGLE-CALL ANSWER for 'how is revenue this year vs last year by month?' / 'show me 2026 vs 2025 monthly trends'. Returns { metric, currentYear, compareYear, rows, totals, charts, presentation, seeAlso }. `rows` is one entry per month with current/compare/change. `charts[0]` is a ready-to-render grouped-bar spec (seriesField='year') — drop straight into a chart library. `totals` gives the year-to-date sum + YoY %. Reads pre-aggregated calendar-month snapshots so it's clean and quick. DEFAULT TO RENDERING THE CHART for any 'X this ye"
      },
      {
        "name": "get_forecast",
        "description": "Forecast a metric — answers 'what should I expect?' / 'are we on track this month?' / 'project next month'. Returns a per-store `stores` array (each in its OWN currency — never summed across currencies). Each store has: `currentMonth` (month-to-date actual + projected month-end with low/high band, built from the live daily run-rate), `horizon` (future full months with expected/low/high), `method`, `confidence`, explicit `assumptions`, `historyMonths`, and a ready-to-render `charts[0]` line spec (seriesField='series' splits actual vs forecast). HONESTY: every forecast carries a method (month_to"
      },
      {
        "name": "save_target",
        "description": "Store a merchant's GOAL for a metric over a calendar period ('we need £80k this month'). TRIGGER: whenever the user states a target/goal/budget for a metric. This saves the merchant's own target in our DB (NOT a write-back to any connected tool). Pacing (are we on track?) is computed later on read by get_targets — never stored. Money metrics (revenue, aov, ltv, …) MUST be scoped to one store via `store` because the workspace can run multiple currencies; pass targetValue in MAJOR units (80000 for £80k). Non-money metrics (orders, cvr, refund_rate, …) may be workspace-wide (omit store). Setting "
      },
      {
        "name": "get_targets",
        "description": "List the merchant's active targets with LIVE actual-vs-target pacing, recomputed from ground truth on every call (never stored). Each target returns a `pacing` block: status (ahead / on_track / at_risk / behind / too_early / not_paceable), actualToDate, projected month-end with its interval, progressPercent, gapToTarget, currentDailyPace vs requiredDailyRunRate, and days elapsed/remaining. Live month-pacing is available for monthly revenue/orders targets scoped to a store; other targets return the goal and defer to get_yoy_monthly / query_metric_snapshots. HONESTY: the status band is derived f"
      },
      {
        "name": "get_daily_metrics",
        "description": "Get daily/period numbers from one of three domains — pass `domain` to pick which, `report` to pick the report within it. `report:\"daily\"` exists in ALL THREE domains and means a DIFFERENT payload in each — always pass both `domain` and `report` together, never assume \"daily\" behaves the same across domains.\n\n• domain:\"funnel\" — the daily series WITH sessions/CVR/funnel steps. Reports: daily (default — { period, data, charts, presentation }, each day has revenue, sessions, visitors, cvr, aov, orders, addToCart, reachedCheckout; DEFAULT TO RENDERING THE RELEVANT CHART), traffic_breakdown (Shopif"
      },
      {
        "name": "get_customer_insights",
        "description": "Get customer behavior and retention insights. All reports are per-store in multi-store mode — use the store param to target a specific store. Reports: new_vs_returning (revenue/orders/AOV split by first-time vs repeat buyers), repeat_metrics (repeat purchase rate, avg days between purchases, BOTH avgLifetimeValue and medianLifetimeValue, lifetimeValueSkewRatio + distribution note — LEAD WITH MEDIAN when describing 'the typical customer', use mean only when distribution is symmetric; if `lifetimeValueDistributionNote` is present the mean is misleading), cohorts (monthly acquisition cohorts — co"
      },
      {
        "name": "get_refunds",
        "description": "Get refund/return analytics. Reports: summary (total refunds, refund rate, % of revenue — supports compare for WoW/MoM/YoY trend), top_products (most refunded products by amount or rate), by_reason (classifies refund notes into buckets: size_fit_too_small, size_fit_too_large, quality_defect, wrong_item, shipping_late_or_lost, color_style_mismatch, comfort, changed_mind, duplicate, out_of_stock — with top affected products and sample verbatim notes per bucket. The out_of_stock bucket is a fulfilment/overselling signal, not a customer preference. Best for diagnosing high refund rates)."
      },
      {
        "name": "get_discounts",
        "description": "Get discount code analytics. Reports: summary (usage rate, AOV impact), top_codes (best performing codes by revenue/usage), code_details (specific code performance — requires 'code' param), by_product (which discount codes were applied to which products — optionally filter by 'code')."
      },
      {
        "name": "get_velocity",
        "description": "Get sales-velocity analytics from one of two angles — pass `angle` to pick which, `report` to pick the report within it.\n\n• angle:\"inventory\" — the INVENTORY-management angle: how fast stock is moving and what to reorder. Reports: summary (sell-through rate, turnover, health status), by_product (products sorted by sales velocity), restock (reorder quantity recommendations from velocity, lead time, and safety stock — pass leadTimeDays/safetyStockDays; the reason to pick this angle). Use when the question is \"what should I reorder and how much?\".\n• angle:\"product\" — the PRODUCT angle: how indivi"
      },
      {
        "name": "get_marketing_performance",
        "description": "Get marketing performance dashboard — INCLUDING the canonical checkout funnel. ALSO USE THIS for 'checkout funnel' / 'where do customers drop off' / 'how is my funnel performing' questions; do NOT manually stitch a funnel from get_analytics + get_store_summary. Returns: `funnel` (sessions, addToCartRate, checkoutRate, purchaseRate, cvr — sessions sourced from Shopify TrafficStat when available, GA4 fallback otherwise, pixel as last resort), `_pixelFunnel` (Triple Whale pixel-tracked equivalents with step-by-step ratios for drop-off SHAPE analysis), `channels` (revenue/sessions/cvr per channel "
      },
      {
        "name": "get_spend_reconciliation",
        "description": "Reconcile ad-spend numbers between Triple Whale's pixel attribution and the ad-platform APIs (Google Ads, Meta, TikTok). Same channel, different numbers — pixel typically captures 10-30% of true spend due to ad blockers, consent banners, and iOS ATT. Returns per-channel platform-API spend, TW pixel spend, ratio, severity (match / moderate / severe / critical), trueRoas (TW revenue / platform spend), pixelRoas (from TW), and a recommendation per channel. Use whenever quoting ROAS — pixel ROAS in isolation is reliably wrong on paid channels with bad pixel coverage."
      },
      {
        "name": "get_products_by_channel",
        "description": "Get products with marketing channel attribution showing which channels drive sales for each product. Attribution uses Triple Whale last-click data joined to Shopify order line items — requires Triple Whale to be connected. When Triple Whale is not connected, all orders appear under 'unattributed'. Use for questions like 'Which channels drive sales of product X?' or 'What products does Facebook sell?'"
      },
      {
        "name": "get_cart_affinity",
        "description": "Co-purchase / market-basket analysis. Given an anchor product (productTitle), returns other products frequently bought with it, ranked by lift. Reports: basket (same-order co-purchase — best for bundles, 'frequently bought together' widgets, post-checkout upsells), lifetime (same-customer across all their orders — best for email flows and longer-horizon recommendations; also returns avgDaysToCoBuy for sequencing). Each pair has support, confidence, lift (>3 strong, 1.5–3 moderate, <1.5 weak), and a verdict. Co-occurrences below `minCoOccurrences` are suppressed because lift is unstable on smal"
      },
      {
        "name": "get_store_summary",
        "description": "Get overall store metrics from Shopify order data ONLY: revenue, orders, average order value, items sold, discounts, unique customers — all for the specified period. Includes percentage changes vs prior period. Also returns all-time totals (orders, products, customers). Use this for high-level store performance questions. For an overview spanning ALL connected sources (traffic, email, support, reviews, etc.) use get_complete_dashboard instead."
      },
      {
        "name": "query_orders",
        "description": "List individual orders matching filters: date range, financial status, customerId, min/max order total, order tags — sortable by date or total. Returns one row per order with name, status, fulfillment, orderTotal (formatted), items count, country, tags, and date. Use for 'show me recent orders', 'orders over $500', 'this customer's pending orders', 'orders tagged wholesale'. DISAMBIGUATION: for AGGREGATE order analytics (counts/revenue by status, country, or product) use get_orders; to look up ONE known order by its #name or ID with line items, use get_order."
      },
      {
        "name": "get_order",
        "description": "Look up a specific order with full line item details (product, variant, SKU, quantity, price, vendor). Use orderName for user-facing IDs like '#1001', or orderId for Shopify numeric IDs. Also supports orderIds (array) to look up multiple orders at once — useful for cross-referencing with Triple Whale attribution data."
      },
      {
        "name": "get_orders",
        "description": "Get order analytics by dimension. Reports: by_status (order counts/revenue by financial status), by_country (geographic sales breakdown — supports compare for WoW/MoM/YoY trend, returns top rising/falling countries), by_product (BEST FOR: 'how many customers bought product X?', 'which products have the most unique buyers?', product-level customer counts. Returns uniqueCustomers, orderCount, unitsSold, and revenue per product. Use this whenever the question involves customers AND products together), by_tag (order volume per Shopify order tag with SUDDEN-DROP DETECTION — compares the last 7 days"
      },
      {
        "name": "get_top_products",
        "description": "Get top selling products ranked by revenue or quantity. Set level to 'variant' to break down by variant (size, colour, etc.) — includes SKU, discount, and profit margin when cost data is available. Set level to 'category' for a revenue / COGS / gross-profit / margin-% rollup by product TYPE (e.g. 'Sewing Machines vs Fabric') — the structured answer to category-P&L / margin-by-category questions (margin is computed only over cost-configured units, with a costCoveragePercent per category). Optionally filter variants by productTitle. Supports compare for WoW/MoM/YoY trend — returns top 5 rising/f"
      }
    ],
    "profiled_at": "2026-09-25T11:01:46.114Z"
  },
  "unreachable": false,
  "payment_method": "open"
}