Store platform

Shopify Storefront MCP

A per-store MCP endpoint that lets AI shopping assistants search a store's catalog, read policies and work with a cart.

What it is and why it matters

What it does

• Product search and details
• Store policy and FAQ lookups
• Cart building for an AI shopping agent

Best for

Merchants preparing for AI assistants that shop on behalf of customers.

How to connect it

1. Read Shopify's Storefront MCP documentation to see which tools are currently available and what, if anything, your store has to enable.
2. Audit your catalog for what an assistant will read: product titles, descriptions, variants, GTINs, images, prices and stock.
3. Check that your shipping, returns and payment policies are written clearly on the store, because assistants answer from them.
4. Test with an MCP-capable assistant by pointing it at your store's endpoint as described in the docs, and ask the questions a shopper would ask.

Exact commands and addresses change. Use the official documentation for the current steps.

Questions you can ask it

• Find waterproof hiking boots under 150 dollars in size 42 and tell me which are in stock.
• What is this store's return policy for worn items?
• Compare the two best-selling backpacks by material, weight and price.
• Add the black medium version to a cart and show me the total with shipping.

What it cannot do

• It does not give you analytics or your own sales data. It exposes what a shopper-facing assistant may see.
• It cannot fix poor product data. It only reflects it.
• Checkout and payment capabilities depend on current Shopify and partner support, so confirm what is live before promising anything to customers.

Staying safe

• The data is the public, shopper-facing view of the store, so keep private notes and internal pricing out of product fields.
• Review how cart and checkout actions are authorized before enabling anything that spends a customer's money.
• Monitor what assistants are told about your products and policies, and correct the source data when answers are wrong.

Watch out for: Product data quality decides what an assistant can say about you. Fix titles, attributes and policies first.

Frequently asked questions