How to Use the Upzelo MCP in Mastra AI

Q: How does Mastra AI handle Upzelo customer data?

Mastra AI uses the savecustomer tool to manage records. If a workflow needs to segment or target users, it can automatically retry saving that customer's details until successful.

Q: Can I monitor Upzelo flow sessions with Mastra AI?

Yes, you use getflowsession. The built-in workflow engine monitors the status and will alert you if a session gets stuck or fails at any point in the retention process.

Q: Does Upzelo support subscription updates via Mastra AI?

You can manage this using updatesubscription. If updating the subscription attributes fails (say, due to a temporary network issue), Mastra AI's exponential backoff retries handle it automatically.

Q: Can I list all subscriptions using Upzelo and Mastra AI?

You can call listsubscriptions. If the initial retrieval fails, the framework won't crash; it will automatically retry fetching the full roster of active or archived subscriptions.

Build resilient, self-correcting retention campaigns with Mastra AI.

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Connect Upzelo MCP to Mastra AI

Create your Vinkius account to connect Upzelo to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Upzelo with Mastra AI

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Handling Customer Segmentation and Records

When a customer needs targeting, you use `save_customer` to write or update their segment record. The built-in workflow engine ensures that if the initial save fails, it automatically retries using an alternate endpoint. This level of automatic retry handling means your retention campaigns run reliably, even when external APIs hiccup.

Managing Complex Subscription Lifecycles

You can build a multi-step workflow around subscription changes. For example: if `update_subscription` fails because of an expired card, the flow automatically triggers an admin notification tool instead. Mastra AI's conditional branching handles these failure paths gracefully, making your checkout process resilient.

Initiating and Monitoring Retention Flows

To start a campaign, you call `start_flow`. The workflow engine monitors this session using `get_flow_session` and knows exactly what to do next. It handles the entire process from initiation to completion. This means your complex retention logic runs without manual checks or fallible code paths.

Setup guide

Set up Upzelo 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 Upzelo 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: "upzelo-mcp-client",
  servers: {
    "upzelo-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about Upzelo MCP in Mastra AI

Mastra AI uses the `save_customer` tool to manage records. If a workflow needs to segment or target users, it can automatically retry saving that customer's details until successful.

Yes, you use `get_flow_session`. The built-in workflow engine monitors the status and will alert you if a session gets stuck or fails at any point in the retention process.

You can manage this using `update_subscription`. If updating the subscription attributes fails (say, due to a temporary network issue), Mastra AI's exponential backoff retries handle it automatically.

This MCP Server handles customer records and subscription attributes, specifically touching **customer details** and **subscription attributes**. The workflow engine ensures these updates are handled with necessary retries for reliable operation.

You can call `list_subscriptions`. If the initial retrieval fails, the framework won't crash; it will automatically retry fetching the full roster of active or archived subscriptions.

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