How to Use the Shopline MCP in Mastra AI

Q: Can Mastra AI handle Shopline product updates in multi-step flows?

Yes. You can define workflows that first call getproductdetails to pull current data, and then run subsequent steps based on whether the retrieved data meets certain criteria. It’s great for complex auditing.

Q: What is the best way to manage Shopline customer records with Mastra AI?

You can use listcustomers as a starting point in your workflow. If you need to process groups of customers, you define a loop within the agent's logic. This makes handling thousands of profiles straightforward and fault-tolerant.

Q: What type of data does this Shopline MCP Server touch regarding store structure?

It touches core store metadata like getshopinfo, as well as product and collection structures via listproducts and listcollections. It gives you a full view of the storefront's organization.

Build resilient, multi-step Shopline workflows with Mastra AI.

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Connect Shopline MCP to Mastra AI

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

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Setup Shopline with Mastra AI

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Automate Order Validation and Follow-Up

You can set up a workflow that first calls `get_order_details` to check an order's status. If the payment fails, Mastra AI automatically triggers a retry step using conditional logic. It even handles notifying an admin if multiple attempts fail. This is great for complex fulfillment pipelines. The framework manages exponential backoff and retries; you just define the sequence of actions. This MCP Server makes sure your agent doesn't quit when things get complicated.

Manage Product Inventory Across Collections

Write a flow that first uses `list_collections` to identify all relevant groups, then iterates through them calling `list_products`. If any product fails the inventory check, your Mastra AI agent logs the failure and moves on gracefully, without stopping the entire process. This handles cross-system dependencies. You tell it: 'Check products in Collection A, then check products in Collection B.' The built-in workflow engine ensures every step runs even if the previous one throws an error.

Build Complete Shop Auditing Workflows

Need a full store health check? You can chain together `list_shop_info`, followed by listing all customers (`list_customers`), and finally pulling product lists. Mastra AI executes this as one cohesive, failure-tolerant workflow. This is for complex maintenance tasks. The agent doesn't just run tools; it interprets the results. For example, if `get_shop_info` shows a missing field, your workflow can automatically flag that issue and stop, giving you actionable reports.

Setup guide

Set up Shopline MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Shopline tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "shopline-mcp-client",
  servers: {
    "shopline-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Shopline Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Shopline tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Shopline transactions"
);
console.log(result.text);

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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.

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Common questions about Shopline MCP in Mastra AI

The framework handles reliability through built-in retries with exponential backoff. If the `list_orders` tool fails due to a transient network blip, your workflow automatically attempts it again until successful or until you hit a defined limit.

Yes. You can define workflows that first call `get_product_details` to pull current data, and then run subsequent steps based on whether the retrieved data meets certain criteria. It’s great for complex auditing.

You can use `list_customers` as a starting point in your workflow. If you need to process groups of customers, you define a loop within the agent's logic. This makes handling thousands of profiles straightforward and fault-tolerant.

Mastra AI is designed for failure tolerance. If one tool call fails—say `list_products` times out—the workflow logs the error, alerts you, and then continues to execute subsequent steps if they don't rely on the failed data.

It touches core store metadata like `get_shop_info`, as well as product and collection structures via `list_products` and `list_collections`. It gives you a full view of the storefront's organization.

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