Mindcase for E-Commerce Price Monitoring

A brand selling across channels doesn't have one price to watch. It has a listing on Amazon, a storefront on Shopify, and — if it's stocked in India — a price at every quick-commerce dark store that carries it. Three platforms, three different input shapes, and no single dashboard that already shows all of them together. An agent that watches pricing across a brand's real footprint has to speak three separate APIs and merge the result itself.

If you're an agent (or building one) reading this rather than a human, the full machine-readable schema for every endpoint below lives at mindcase.co/skills.md.

What does cross-channel price monitoring actually require?

Each platform answers a different question, and a brand needs all three answered together, not separately:

• Amazon — what's the price right now, and is a discount currently live? Amazon Products reads this straight off the listing.
• Shopify — is the brand's own storefront underselling or overselling its Amazon listing? Shopify Products reads any Shopify-powered store directly, no login required.
• Quick-commerce — is a specific dark store quietly discounting below MRP, or out of stock while a competitor two kilometers over isn't? Covered in full in Turning Blinkit and Zepto Prices into a Live Feed — this post picks up where that one leaves off rather than repeating it.

A brand that only watches Amazon misses its own storefront quietly undercutting itself, or a reseller breaking MRP in a city its team never checks by hand.

How do you pull the same brand's products across Amazon and Shopify?

Same brand, two calls, two different input shapes — Amazon takes a product URL or ASIN, Shopify takes a store URL and crawls its catalog directly.

curl -X POST "https://api.mindcase.co/v1/data/amazon/products/run?wait=true" \
-H "Authorization: Bearer $MINDCASE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "params": {
    "urls": ["https://www.amazon.com/dp/B0CHX3QBCH"]
  }
}'

# $0.0015 per product

Amazon Products returns 36 fields per listing — price, list price, discount, availability, and full product detail. For the brand's own storefront, point Shopify Products at the store URL instead:

curl -X POST "https://api.mindcase.co/v1/data/shopify/products/run?wait=true" \
-H "Authorization: Bearer $MINDCASE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "params": {
    "urls": "https://allbirds.com",
    "maxResults": 100
  }
}'

# $0.001 per product

Shopify Products takes a full store URL and crawls its catalog directly — no separate call per product. It returns 21 fields: price range (priceFrom/priceTo), sale status, stock, variants, and the product's full option set. Point it at one product URL instead of a store URL and it returns just that listing.

How do you set this up as an agent workflow?

The three calls above don't merge themselves — that's the agent's job, not the API's. Give an agent the brand's Amazon ASINs, Shopify store URL, and the quick-commerce lookup from the linked post, and have it run all three on a schedule, writing each pull to one table keyed by sku, channel, and pulled_at. From there, the comparison an agent actually cares about is simple: is the Shopify price lower than the live Amazon price for the same SKU right now, and is any quick-commerce store showing a price below either one. Point Claude Code or another coding agent at mindcase.co/skills.md and it can wire this up directly — the schema for all three endpoints is machine-readable, so the agent doesn't need this post explained to it, just the URL.

What does a cross-channel comparison actually catch?

Say a skincare brand sells a $28 moisturizer on Amazon, runs its own Shopify store, and is stocked through Instamart and Zepto in a handful of Indian cities. A merged pull across all three might show: Amazon at $28.00 with no active discount, the brand's own Shopify store at $24.99, and one Instamart dark store showing the equivalent price below MRP while every other store nearby holds it. Neither the Amazon call nor the Shopify call nor the quick-commerce call flags any of that alone — each one just reports its own current price. The signal only shows up once an agent lines the three rows up side by side: the brand's own storefront is quietly undercutting its Amazon listing by 11%, and one specific store is breaking MRP while its neighbors aren't. That's the kind of thing a brand finds out from a customer complaint without this running on a schedule.

What can't you get from Mindcase?

Two cases.

A unified SKU mapping across platforms. Amazon, Shopify, and quick-commerce each use their own product identifiers, and nothing maps them together automatically. That match — "this Amazon ASIN and this Shopify product ID are the same physical item" — is business knowledge only the brand has; an agent has to be given that mapping, not infer it.

Price history from before you started watching. Same limitation as the individual Amazon and quick-commerce posts: none of these platforms publish a history endpoint. Every channel in this workflow only tells you the present moment — your monitoring feed's timeline starts the day you turn it on.

Which endpoint should you use for which job?

FAQ