How to Use the Shopline MCP in Vercel AI SDK

Q: How do I use Shopline and TypeScript SDK together for data display?

You initialize the client using mcpClient.tools() and then pass those tools to your streaming function, like streamText. This allows your agent to call specific methods on the MCP Server and stream the resulting data directly into your web components.

Q: What kind of data can I get about a Shopline store using this MCP Server?

You can retrieve basic shop details with getshopinfo. You also have tools to list all products, collections, and customers. Basically, it covers the main administrative aspects of running the storefront.

Q: Can I check order status using Shopline and TypeScript SDK?

Yes. Use getorderdetails by passing a specific order ID. The MCP Server returns all the necessary transaction details, allowing you to build accurate, real-time tracking UIs for your users.

Q: What type of data does this Shopline MCP Server touch regarding user accounts?

It handles listcustomers and provides tools like getorderdetails, meaning you're interacting with customer profiles and order records. It keeps your agent connected to core account management.

See Shopline data stream live in your UI with the TypeScript SDK.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Shopline MCP to Vercel AI SDK

Create your Vinkius account to connect Shopline to Vercel AI SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup Shopline with Vercel AI SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Display Real-Time Store Data

You can pull basic shop information using `get_shop_info`. This is perfect for showing things like the store's name or main contact details right when the user lands on your page. Because of the SDK's streaming nature, the data appears in the UI as soon as it hits; you don't wait for a loading spinner to vanish. It makes building highly interactive product pages simple. Just call `get_product_details` and stream that inventory count or description directly into your React component. The user sees the numbers changing live, which is key for good UX.

Audit Inventory in Real-Time

Need a full product catalog list? Use `list_products` to get every item available across Shopline. This method returns clean data you can map directly into a searchable grid or gallery view. You'll see all the necessary fields, from SKU numbers to current stock levels. If your app needs to check specific items, run `get_product_details`. The streaming architecture of this MCP Server means that even complex queries feel instant. It’s ideal for building inventory dashboards where data freshness matters.

Track Customer Profiles Instantly

When a user needs to check an account, run `list_customers` to pull the necessary profile information. You get back details like names and emails that you can use immediately in your client-side code. The data structure is clean, making mapping it into forms or dashboard widgets pretty straightforward. For order history display, call `list_orders`. This gives you a chronological list of purchases for the user. Since this MCP Server supports streaming, you'll see the order records appear on your screen as they process, which feels really fast to the end-user.

Setup guide

Set up Shopline MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Shopline tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Shopline transactions",
});

console.log(text);
await mcpClient.close();

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Shopline. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Shopline now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Shopline MCP in Vercel AI SDK

You initialize the client using `mcpClient.tools()` and then pass those tools to your streaming function, like `streamText`. This allows your agent to call specific methods on the MCP Server and stream the resulting data directly into your web components.

You can retrieve basic shop details with `get_shop_info`. You also have tools to list all products, collections, and customers. Basically, it covers the main administrative aspects of running the storefront.

The streaming approach handles this well. You can sequence API calls—for example, listing collections then getting product details for each one—and the data will appear sequentially in your UI without needing to wait for massive loading blocks.

Yes. Use `get_order_details` by passing a specific order ID. The MCP Server returns all the necessary transaction details, allowing you to build accurate, real-time tracking UIs for your users.

It handles `list_customers` and provides tools like `get_order_details`, meaning you're interacting with customer profiles and order records. It keeps your agent connected to core account management.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript