How to Use the Zeplo (Queue & Background Job API) MCP in Mastra AI

Q: Can I schedule recurring tasks in Mastra AI using Zeplo (Queue & Background Job API)?

Yes, you use createschedule to define the recurrence pattern. Then, your agent uses updateschedule to adjust that timing or temporarily pause a job if maintenance is required. This gives predictable background execution.

Q: Does Zeplo (Queue & Background Job API) support credential management in Mastra AI?

It does. The createtoken tool generates the necessary access token that your workflow uses to authenticate against the MCP Server, ensuring every job run has proper authorization.

Build Resilient Workflows with Zeplo (Queue & Background Job API) for Mastra AI.

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Setup Zeplo (Queue & Background Job API) with Mastra AI

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Building Multi-Step Workflows on the MCP Server

Mastra AI can manage complex, multi-step processes using dedicated queues. The agent calls `enqueue_request` to start a task that needs several stages of processing, and then uses tools like `get_queue` to confirm its overall status. This is perfect for workflows requiring conditional branching—if the first step fails, the second job can be scheduled via `create_schedule` without human intervention.

Handling Failures and Retries with Zeplo (Queue & Background Job API)

The MCP Server provides tools to manage failure states. If a process stalls, the agent can check the timeline using `get_request` or manually intervene by calling `update_schedule` to pause or resume the workflow. This allows your Mastra AI workflows to automatically retry jobs after a specified delay, ensuring high uptime and graceful failure handling.

Controlling Scope with MCP Server Tokens

To maintain security across complex deployments, the agent uses `create_token` to generate short-lived, single-use access credentials. These tokens are then passed to subsequent calls like `list_tokens`, limiting what any part of the workflow can access. It's also useful for restricting scope by listing available resources with tools such as `list_queues` before running critical jobs.

Setup guide

Set up Zeplo (Queue & Background Job API) 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 Zeplo (Queue & Background Job API) 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: "zeplo-queue-background-job-api-mcp-client",
  servers: {
    "zeplo-queue-background-job-api-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Zeplo (Queue & Background Job API) transactions"
);
console.log(result.text);

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Common questions about Zeplo (Queue & Background Job API) MCP in Mastra AI

It separates the execution from the control flow. When a step needs time or retries, the agent uses `enqueue_request` to hand it off to the MCP Server. The workflow engine then monitors this job's status using tools like `get_queue`.

Yes, you use `create_schedule` to define the recurrence pattern. Then, your agent uses `update_schedule` to adjust that timing or temporarily pause a job if maintenance is required. This gives predictable background execution.

The agent can check the full history of any resource by calling `list_queue_logs` for a specific queue. You'll get paginated details showing exactly what happened, when it failed, and why.

It does. The `create_token` tool generates the necessary access token that your workflow uses to authenticate against the MCP Server, ensuring every job run has proper authorization.

The server deals with operational metadata: queue names, schedule definitions, team member invitations (`invite_team_member`), and temporary access tokens. It manages the job infrastructure, not the core business data.

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