Demo Shopify Storefront MCP Server
LocalAbout Demo Shopify Storefront MCP Server
Demo Shopify Storefront MCP Server is a demo Express server configured with a Shopify domain and Storefront API token. The README covers running or deploying it and connecting a client, but does not detail the tools it exposes.
Key features
- 01Shopify domain and token configuration
- 02Express server setup
- 03Local or remote deployment
- 04MCP client connection instructions
Use cases
- 01Configure a demo with Shopify credentials
- 02Run the server locally
- 03Test a remote endpoint with MCP Inspector
MCP support
Runs as an MCP server that clients can reach at the /mcp endpoint; the README does not specify its available tools.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEDemo Shopify Storefront MCP Server
This is created as a demo for a proposal regarding Public MCP Servers on the OpenAI Community Platform
Setup
cp .env.example .env
then, replace with env vars with your Shopify domain and your Shopify Storefront API Token
Running
# First terminal
npm build
npm start
Deployment of the Public Server
You can deploy the Express Server whereever you wish and then use https://your-domain.com/mcp instead of http://localhost:3000/mcp for the following examples to test the remote server.
Connecting via Claude Desktop
First run the MCP Server like above. Then add this to your Claude Desktop Config
{
"mcpServers": {
"demo-shopify-storefront-mcp-server": {
"command": "npx",
"args": [
"-y",
"mcp-remote@latest",
"http://localhost:3000/mcp"
]
}
}
}
Manual Testing
# Second terminal
npx @modelcontextprotocol/inspector http://localhost:3000/mcp
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.