How to Use the Jiguang Aurora / 极光 MCP in Vercel AI SDK

Push notifications in your Vercel AI SDK app. Control Jiguang Aurora / 极光 messaging directly from your UI components.

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Connect Jiguang Aurora / 极光 MCP to Vercel AI SDK

Create your Vinkius account to connect Jiguang Aurora / 极光 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.

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Setup Jiguang Aurora / 极光 with Vercel AI SDK

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Real-time push control for Vercel AI SDK

Send notifications straight from your edge functions. Using `send_push` inside your streamText calls lets the interface react immediately to user actions. Your users see status updates as they happen. The AI client handles the delivery without forcing a page refresh or waiting for background jobs to finish.

Device management in your interface

Fetch registration details on the fly. You can call `get_device_info` to verify if a user is reachable before triggering a message. This keeps your notification logic tight. You aren't guessing if the device is active; you're using live data to decide when to ping them.

Monitor delivery performance live

Pull performance metrics into your dashboard. By hitting `get_push_report`, you can visualize engagement trends directly in your React or Svelte app. It removes the need to jump between consoles. You see the success rate of your campaigns as the data comes in.

Setup guide

Set up Jiguang Aurora / 极光 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 Jiguang Aurora / 极光 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 Jiguang Aurora / 极光 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 Jiguang Aurora / 极光. 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.

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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

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Single dashboard

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place for every integration

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

Common questions about Jiguang Aurora / 极光 MCP in Vercel AI SDK

Install the MCP client package and connect via HTTP. You then pass the tool definitions directly into your generateText function to start executing commands.

Yes, you manage the request flow within your application code. You should implement your own retry logic if you hit quota ceilings.

It is, provided you secure your endpoint tokens. The communication happens over encrypted channels and handles standard API authentication.

The server provides device registration status, message delivery success rates, and account usage quotas. You only get what the API returns.

Your device registration IDs are handled ephemerally. The Vercel AI SDK processes these tokens in memory during the execution of your tools.

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