How to Use the Zeabur (PaaS Deployment) MCP in Mastra AI

Q: What kind of data does Zeabur (PaaS Deployment) touch when Mastra AI sends emails?

The server touches the email content and requires a ZEABUREMAILTOKEN. It processes both single transactional messages and large batches.

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Connect Zeabur (PaaS Deployment) MCP to Mastra AI

Create your Vinkius account to connect Zeabur (PaaS Deployment) 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 Zeabur (PaaS Deployment) with Mastra AI

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Perplexity

Mastra AI: Complex Workflows and Deployments

The `deploy_template` tool lets you deploy a service using YAML, which is key for building those complex workflows. You can also use `create_upload_stage` to stage assets before the deployment happens. When things fail in your workflow, you rely on the MCP Server's ability to manage containers and execute commands like `execute_command`.

Mastra AI: Monitoring and Debugging Services

Keep an eye on what’s going down with your deployed services. Use `get_build_logs` to fetch the build logs, or grab a specific file using `download_file`. You can also check deployment readiness by calling `prepare_deployment`.

Mastra AI: Messaging and Scheduled Communication

The MCP Server handles all your messaging needs. Send immediate emails with `send_email`, or send bulk, personalized messages using `send_batch_emails`. Don't forget to use `schedule_email` if the communication isn't time-sensitive.

Setup guide

Set up Zeabur (PaaS Deployment) 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 Zeabur (PaaS Deployment) 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: "zeabur-paas-deployment-mcp-client",
  servers: {
    "zeabur-paas-deployment-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about Zeabur (PaaS Deployment) MCP in Mastra AI

The MCP Server provides the underlying capability for your workflow engine. It handles tasks like deploying services and managing containers, allowing for conditional branching based on success or failure.

Yes. The MCP Server manages the state of the deployment. If a step fails, your framework can retry using `deploy_template` or other container tools.

The agent calls multiple MCP tools sequentially. For instance, it might `create_upload_stage` first, then call `deploy_template`, and finally run a check using `get_build_logs`.

The server touches the email content and requires a `ZEABUR_EMAIL_TOKEN`. It processes both single transactional messages and large batches.

The connection uses the MCP Server's standard endpoint token. This keeps authentication handled at a central point, securing your agent's access to deployment resources.

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