How to Use the JD Cloud Infrastructure MCP in Vercel AI SDK

Q: How do I configure the JD Cloud Infrastructure MCP Server inside a Vercel AI SDK project?

Install @ai-sdk/mcp and initialize the client using createMCPClient with your Vinkius HTTP endpoint. Pass the tools directly into the tools parameter of generateText or streamText.

Q: How does Vercel AI SDK handle long-running JD Cloud Infrastructure operations like rebooting a VM?

Your agent triggers rebootvminstance and streams the immediate response. If you need to monitor the transition, the agent can poll describevminstance and stream status updates to the UI.

Q: What is the best way to handle JD Cloud Infrastructure errors in Vercel AI SDK?

Wrap your streamText call in a standard try-catch block. If a tool like describeclouddisk fails due to an invalid disk ID, the SDK catches the error, allowing your agent to explain the issue to the user.

Stream JD Cloud Infrastructure metrics and VM states directly into your Vercel AI SDK frontend in real time.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect JD Cloud Infrastructure MCP to Vercel AI SDK

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

GDPR Free for Subscribers

Setup JD Cloud Infrastructure with Vercel AI SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Stream live JD Cloud VM status with Vercel AI SDK

Stop making your users stare at static tables or spin-loops. This MCP Server lets your Vercel AI SDK application fetch active virtual machines using `list_vm_instances` and stream the raw state directly to the client's screen as it arrives. If an instance is acting up, the user can trigger `describe_vm_instance` to get granular details without a hard page reload. The integration is straightforward. You instantiate the client, grab the tools, and feed them directly into `streamText`. Your frontend updates on the fly, rendering live node statuses and network configurations the second the JD Cloud API responds.

Render instant disk and DB telemetry

Edge functions need to be fast. By combining Vercel AI SDK with this MCP Server, your edge routes can query system telemetry using `describe_metric_data` and stream the data points directly to a React or Svelte chart. There is zero middleman latency because the tool results flow straight into your UI component. You can also list database resources with `list_rds_instances` or check attached storage using `list_cloud_disks` in the same edge request. Your agent parses the payload, extracts the metrics, and updates the dashboard view before the connection closes.

Control JD Cloud resources with interactive UI tools

Giving users control doesn't mean building complex backend APIs. Use `stop_vm_instance`, `start_vm_instance`, or `reboot_vm_instance` as tools inside your streaming chat interface via this MCP Server. The client executes the action and immediately streams the confirmation state back to the user interface. Always remember to call `mcpClient.close()` when the session ends to prevent memory leaks in your serverless environments. This ensures your edge runtime stays lean while managing high-throughput operations.

Setup guide

Set up JD Cloud Infrastructure MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all JD Cloud Infrastructure tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent JD Cloud Infrastructure transactions",
});

console.log(text);
await mcpClient.close();

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by JD Cloud. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect JD Cloud Infrastructure now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about JD Cloud Infrastructure MCP in Vercel AI SDK

Install `@ai-sdk/mcp` and initialize the client using `createMCPClient` with your Vinkius HTTP endpoint. Pass the tools directly into the `tools` parameter of `generateText` or `streamText`.

Yes, the server runs on Vinkius, meaning your Edge Functions only make lightweight HTTP calls. This keeps your bundle size small and avoids cold starts when running `list_elastic_ips` or `list_oss_buckets`.

Your agent triggers `reboot_vm_instance` and streams the immediate response. If you need to monitor the transition, the agent can poll `describe_vm_instance` and stream status updates to the UI.

Wrap your `streamText` call in a standard try-catch block. If a tool like `describe_cloud_disk` fails due to an invalid disk ID, the SDK catches the error, allowing your agent to explain the issue to the user.

Vinkius handles your API credentials in an isolated V8 sandbox, meaning your Vercel AI SDK code never directly handles or exposes the raw access keys. Only the final tool outputs, like disk lists or database metadata, are sent to your frontend.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript