How to Use the JD Cloud Infrastructure MCP in Mastra AI

Q: How do I register the JD Cloud Infrastructure tools in a Mastra AI agent using this MCP Server?

Initialize the client using new MCPClient and specify your Vinkius server URL. Retrieve the tool list with mcpClient.listTools() and spread them directly into your agent's tool configuration array.

Q: How does Mastra AI handle human-in-the-loop approval for JD Cloud Infrastructure actions?

You can configure the requireToolApproval option on specific tools like stopvminstance. This pauses execution and prompts an administrator before the agent can shut down any production virtual machine.

Q: Can I run conditional branching based on JD Cloud Infrastructure metrics in Mastra AI?

Yes. Your workflow can fetch telemetry using describemetricdata and branch based on the results. If disk space is low, the workflow can trigger alerts; otherwise, it continues normal operations.

Build resilient, self-healing JD Cloud Infrastructure workflows with Mastra AI agents and automated retries via MCP.

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Connect JD Cloud Infrastructure MCP to Mastra AI

Create your Vinkius account to connect JD Cloud Infrastructure 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 JD Cloud Infrastructure with Mastra AI

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Build self-healing JD Cloud Infrastructure tasks

When a server goes down, you want automated remediation, not manual intervention. Mastra AI lets you write workflow pipelines that run `list_vm_instances` to identify stopped nodes and automatically execute `start_vm_instance` to recover them. If the action fails, Mastra's built-in retry engine handles backoffs automatically. You can structure complex branching logic to handle database issues too. For instance, if `list_rds_instances` flags a high-load database, your workflow can query performance telemetry using `describe_metric_data` to decide whether to alert an engineer or trigger a reboot.

Approve sensitive storage actions in Mastra AI

Deleting or modifying disks shouldn't happen without verification. By using Mastra's `requireToolApproval` feature with this MCP Server, your agent can inspect volumes using `list_cloud_disks` and draft a cleanup plan. The agent waits for manual confirmation before executing any dangerous changes. This keeps your cloud environment clean without risking accidental data loss. Your agent checks `describe_cloud_disk` to confirm the disk is unattached, then pauses for human approval before proceeding.

Run automated security and resource audits

Unused resources waste money and create security risks. Mastra workflows can schedule daily audits that run `list_elastic_ips` and `list_oss_buckets` to find unassigned addresses and public storage buckets using the MCP Server. The agent compiles these findings into a structured report. Because Mastra runs workflows as state machines, you can track the audit progress and resume the pipeline if a network timeout occurs mid-run.

Setup guide

Set up JD Cloud Infrastructure 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 JD Cloud Infrastructure 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: "jd-cloud-infrastructure-mcp-client",
  servers: {
    "jd-cloud-infrastructure-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about JD Cloud Infrastructure MCP in Mastra AI

Initialize the client using `new MCPClient` and specify your Vinkius server URL. Retrieve the tool list with `mcpClient.listTools()` and spread them directly into your agent's tool configuration array.

Yes, Mastra's workflow engine features built-in retry logic with exponential backoff. If a call to `reboot_vm_instance` fails due to transient API rate limits, the system retries the command automatically.

You can configure the `requireToolApproval` option on specific tools like `stop_vm_instance`. This pauses execution and prompts an administrator before the agent can shut down any production virtual machine.

Yes. Your workflow can fetch telemetry using `describe_metric_data` and branch based on the results. If disk space is low, the workflow can trigger alerts; otherwise, it continues normal operations.

Your sensitive API credentials reside inside the secure Vinkius V8 Isolate Sandbox. Mastra AI only interacts with the exposed MCP tools, ensuring your underlying cloud secrets are never exposed to the agent framework.

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