Prompt library
These prompts are written in business language, not tool syntax. Replace placeholders such as {shopName}, {period}, and {productName} when you know them. If you do not specify a shop, the assistant should discover the shops available to your DataGlass account before continuing.
If you are new to the connector, use the interactive prompt lab to build a question and preview its tool route. The worked examples then show six complete answers with evidence and quality checks.
Most of these prompts are answered by reading alone. A few end in a proposed change — a target ROAS, a price move, a reorder. Those only execute if your connector holds that domain's write scope and you approve the specific change; see Permissions and safety.
1. Get started and discover shops
Native playbook: getting-started
Workflow: retrieve get_playbook when guidance is needed, then use one or more of list_shopee_shops, list_lazada_shops, and list_tiktok_shops.
| Scenario | Prompt |
|---|---|
| First connection | Show me all of my connected shops and group them by marketplace. |
| Choose a shop | Which of my shops can I analyze, and what region and currency does each use? |
| Understand coverage | What can DataGlass analyze for my Shopee, Lazada, and TikTok shops? |
| Selected workspace | Give me a quick guide to the data available for {shopName}. |
Good answer: show names and platforms first. Keep raw IDs as supporting metadata unless the user requests them.
2. Check shop health
Native playbook: shop-health-check
Workflow: discover the shop, then call diagnose_shop. Follow a signal's suggestedTool only when more evidence is needed. Use get_financial_overview for an all-shops rollup.
| Scenario | Prompt |
|---|---|
| Selected shop snapshot | How is {shopName} doing over the last 30 days? Flag anything that needs attention. |
| Week-over-week check | Give me a health check for {shopName} this week versus the previous week. |
| Risk scan | Is anything wrong with {shopName} right now? Focus on profit, GMV, margin, COGS coverage, and loss-makers. |
| Portfolio view | Summarize realized profit and margin across all of my shops for {period}. |
| Executive summary | Give me the three most important things I should know about {shopName} today. |
Good answer: lead with a one-line conclusion, label warning or critical signals, and avoid dumping full product tables unless asked.
3. Explain a profit, GMV, or sales drop
Native playbook: why-profit-dropped
Workflow: start with diagnose_shop, then use the relevant product ranking, breakdown, cost, COGS, or ads tools. Compare with the immediately preceding equal-length period unless the prompt names another baseline.
| Scenario | Prompt |
|---|---|
| Profit decline | Why did realized profit drop for {shopName} in {period}? Rank the biggest drivers. |
| Sales decline | Sales are down for {shopName}. Tell me whether the cause is fewer orders, lower revenue per order, ads, or product mix. |
| Margin compression | Why is margin lower for {shopName} even though GMV is stable? Break down the cost lines that changed. |
| Product movers | Which products contributed most to the profit change for {shopName} in {period}? |
| Data-quality check | Could the apparent profit drop for {shopName} be caused by missing COGS or settlement lag? |
Good answer: separate real business movement from missing-cost or settlement artifacts, and state the strongest driver first.
4. Improve low-margin or loss-making products
Native playbook: improve-low-margin
Workflow: use the platform financial overview and product breakdown or ranking, confirm get_product_costs, run the relevant profit simulation, then retrieve margin-defense or price-buffer recommendations when useful.
| Scenario | Prompt |
|---|---|
| Find loss-makers | Which products are losing the most money in {shopName} over {period}, and why? |
| Thin-margin review | Find products in {shopName} with dangerously low margin and rank them by profit impact. |
| Remediation options | For the three worst-margin products in {shopName}, compare a price increase, a smaller discount, and a COGS reduction. |
| Cost validation | Before recommending changes, verify whether the worst products have explicit or estimated COGS. |
Good answer: do not recommend a price move based on inferred COGS without clearly stating the uncertainty.
5. Diagnose and optimize ads
Native playbook: debug-ads
Shopee workflow: use get_shopee_ads_performance, True or profit-adjusted ROAS, then the relevant ads, campaign-floor, target-ROAS, GMS, or boost recommendation tools.
TikTok workflow: use get_tiktok_gmv_max_campaigns, optional session drilldown, get_tiktok_campaign_roas, then budget or target-ROAS recommendations.
| Scenario | Prompt |
|---|---|
| Weak campaigns | Which ad campaigns in {shopName} have the weakest true ROAS in {period}? |
| Hidden losses | Find campaigns that look good on marketplace ROAS but lose money after COGS, fees, cancellations, and returns. |
| Budget allocation | Where should I increase, hold, or reduce ad budget for {shopName}? Explain the profit evidence. |
| Bid or target guidance | Show the recommended bid or target ROAS for the weakest campaigns in {shopName}. |
| TikTok drilldown | Review the GMV Max campaigns and product sessions for {shopName} and identify wasted spend. |
Good answer: optimize on True or profit-adjusted ROAS, not headline ROAS. A recommendation is a proposal until someone applies it — in DataGlass, or through a tool such as apply_shopee_target_roas when PAT_ADS_WRITE is granted and the user has agreed to the specific change.
6. Defend margin from commission changes
Native playbook: defend-margin-commission
Workflow: retrieve the platform commission overview and impact, then the matching margin-defense actions and a simulation for selected products.
| Scenario | Prompt |
|---|---|
| Upcoming increase | How much will the upcoming commission change cost {shopName}, by product? |
| Preserve target margin | What price or discount changes would preserve margin after the commission increase for {shopName}? |
| Prioritize action | Rank the products in {shopName} most exposed to the new commission rate and suggest the smallest safe adjustment. |
| Compare tactics | For affected products in {shopName}, compare raising price with trimming discount before I decide. |
Good answer: include the effective date, current and new rate when available, profit impact, and the proposed margin-defense move. If the connector holds PAT_PRICING_WRITE, it can apply that move with apply_margin_defense_action — it should show the per-variant prices and get an explicit go-ahead first.
7. Plan inventory and restocking
Native playbook: plan-restock
Workflow: call get_inventory_alerts, get_reorder_plan, a forecast or demand chart, then get_open_purchase_orders to avoid double ordering.
| Scenario | Prompt |
|---|---|
| Immediate risk | Which SKUs need restocking now, and how urgent is each one? |
| Reorder quantities | Build a reorder plan using current stock, reorder points, forecast demand, and inbound purchase orders. |
| Demand explanation | Explain why {productName} needs its recommended reorder quantity using historical and forecast demand. |
| Overstock review | Which products appear overstocked or slow-moving, and what evidence supports that? |
Good answer: subtract or explicitly show inbound stock and distinguish open alerts from resolved alert history.
8. Audit COGS and margin confidence
Native playbook: check-cogs
Workflow: read shop financial COGS coverage, resolve canonical products with list_products, use get_product_costs, and retrieve get_estimated_margin for Shopee shop-level inference when needed.
| Scenario | Prompt |
|---|---|
| Missing costs | Which products in {shopName} are missing explicit COGS? |
| Profit confidence | How much of {shopName}'s profit in {period} depends on estimated rather than entered COGS? |
| Cost audit | Verify the stored cost basis for {productName} and explain how it affects margin. |
| Prioritize cleanup | Rank the missing-COGS products by how much they could distort reported profit. |
Good answer: label explicit, inferred, and missing costs separately. Users enter costs in the DataGlass app, not through the MCP connector.
9. Simulate price, discount, or cost changes
Native playbook: simulate-price-change
Workflow: locate the product, read its current economics, then call exactly one of simulate_shopee_profit, simulate_lazada_profit, or simulate_tiktok_profit for each proposed scenario.
| Scenario | Prompt |
|---|---|
| Price increase | If I raise {productName}'s price by 5%, what happens to per-unit profit and margin? |
| Discount reduction | Compare the current result for {productName} with reducing its discount from 15% to 10%. |
| COGS negotiation | How would {productName}'s profit change if unit COGS fell by {currency} 10? |
| Break-even target | What selling price would {productName} need to reach a 20% margin, based on its current fees and COGS? |
Good answer: show current versus simulated values and list the assumptions. Never claim that the live listing was changed.
10. Analyze products and conversion
Native playbook: none yet.
Workflow: discover the platform shop, use a platform product list or search, then the matching conversion-metrics tool. Use fetch for a complete canonical product after search.
| Scenario | Prompt |
|---|---|
| Product lookup | Find {productName} across all of my connected shops and show its platform listings. |
| Funnel diagnosis | Which products in {shopName} have high traffic but weak conversion in {period}? |
| Platform comparison | Compare the conversion performance of {productName} across Shopee, Lazada, and TikTok where it is listed. |
| Discover opportunity | Find products with improving conversion but low sales volume that may deserve more visibility. |
Good answer: use YYYYMMDD windows for conversion-tool inputs, but display ordinary calendar dates to the user.
11. Review orders and fulfilment
Native playbook: none yet.
Workflow: use count_orders for a quick count, list_orders for rows, get_order for one normalized order, and count_unshipped_orders for backlog.
| Scenario | Prompt |
|---|---|
| Volume check | How many orders did I receive across all shops in {period}, broken down by platform? |
| Fulfilment backlog | How many orders are still waiting to ship, and how old is the backlog? |
| Order investigation | Find the details and line items for order {order reference}. |
| Recent order list | List my newest orders for {shopName} and highlight unusual totals or statuses. |
Good answer: call the count tool before fetching a long list when the user asks only “how many.” Follow cursors only when more rows are necessary.
12. Reconcile marketplace payouts
Native playbook: none yet.
Workflow: discover the shop and use exactly one of get_shopee_payout, get_lazada_payout, or get_tiktok_payout for the requested payout or settlement dates. Do not substitute profit tools.
| Scenario | Prompt |
|---|---|
| Amount paid | How much did {platform} pay {shopName} in {period}? |
| Reconciliation | Compare DataGlass's expected payout with the marketplace-reported payout for {shopName}. |
| Investigate a delta | Which orders or statements explain the payout mismatch for {shopName} in {period}? |
| Pending versus paid | Separate paid and pending settlement amounts for {shopName} in {period}. |
Good answer: state whether the date is a payout or settlement date, show local currency, and treat a zero reconciliation delta as an exact match. Lazada exposes a net finance-statement settlement rather than the Shopee and TikTok reconciliation report.
13. Explore recommendations and growth opportunities
Native playbooks: debug-ads, improve-low-margin, and defend-margin-commission cover parts of this domain.
Workflow: begin with list_recommendations, then retrieve the typed recommendation tool that matches the decision.
| Scenario | Prompt |
|---|---|
| Recommendation inbox | What are the newest actionable recommendations across all of my shops? |
| Growth ideas | Which products should I boost or bundle to improve discovery and basket size? |
| Margin protection | Show price-buffer and margin-defense recommendations for {shopName}. |
| Ads actions | Summarize the highest-impact ads recommendations for {shopName} and the evidence behind each. |
Good answer: state recommendation status, shop and platform, expected impact, and the evidence. Applying an Action happens in DataGlass, or through a tool such as rollback_recommendation or stop_recommendation when PAT_RECOMMENDATION_WRITE is granted — never without the user's explicit agreement to that specific change.
14. Troubleshoot connection or missing data
Native playbook: none yet.
Workflow: list the platform's shops. For Shopee, use list_shopee_shop_accounts to inspect authorization by app type. Explain authentication, access, plan, and data-timing failures without retry loops.
| Scenario | Prompt |
|---|---|
| Missing shop | Why is one of my connected shops missing from DataGlass? |
| Missing Shopee data | Check whether {shopName}'s Shopee ERP or Ads authorization has expired. |
| Empty result | I expected data for {shopName} in {period}. Check the shop, region, date format, authorization, and settlement timing. |
| Reconnect | My DataGlass connector stopped working. Tell me whether I need to reconnect, re-authenticate, or upgrade my plan. |
Good answer: a 401 requires OAuth re-authentication; a 403 is an ownership or allowlist denial and should not trigger a re-authentication loop. A plan denial should link to DataGlass billing.
15. Explain query conventions
Native playbook: conventions
Workflow: retrieve get_playbook(playbook="conventions") when the model needs wire conventions, then translate the result into ordinary language.
| Scenario | Prompt |
|---|---|
| Date help | What date range will you use for "last month," and what comparison period will you use? |
| Metric help | Explain the difference between realized profit, payout, headline ROAS, and true ROAS. |
| Identifier help | Find the correct shop and product identifiers for my request instead of asking me to look them up. |
Good answer: human dates are YYYY-MM-DD, conversion datadate values are YYYYMMDD, timestamps are ISO 8601, and money uses the shop's local currency.
Prompt template
Use this structure when creating another prompt:
<business question> for <shop or all shops> over <period>.
Show <decision metric> and explain <desired evidence or next step>.
| Placeholder | Use |
|---|---|
{shopName} | Human-readable shop name from the current workspace |
{platform} | Shopee, Lazada, or TikTok Shop |
{period} | Natural language such as “the last 30 days” |
{productName} | Product name or SKU; let the connector resolve IDs |
{currency} | The shop's local currency, when already known |
{campaignName} | Campaign name, or ask for the weakest campaigns |