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How to Use the Jiguang Aurora / 极光 MCP in Mastra AI

Automate messaging workflows in Mastra AI. Jiguang Aurora / 极光 handles your push logic.

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Connect Jiguang Aurora / 极光 MCP to Mastra AI

Create your Vinkius account to connect Jiguang Aurora / 极光 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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Build automated notification trees in Mastra AI

Chain your messaging logic into complex workflows. You can use `create_schedule` to set up recurring alerts that fire based on agent decisions. If a step fails, the workflow engine handles the retry. You define the logic once, and the server executes the delivery timeline.

Conditional messaging for complex agents

Use `get_message_status` to determine the next agent action. If a push fails to deliver, the agent can decide to try a different channel. This makes your agents responsive to the actual state of the notification. You aren't just sending; you're reacting to delivery outcomes.

Manage notification quotas automatically

Check your limits before running a massive campaign. By calling `get_account_quota`, your agents can pause if you are nearing a threshold. It prevents accidental service outages. Your agent stays within bounds without you needing to monitor the dashboard.

Setup guide

Set up Jiguang Aurora / 极光 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. 1

    Install dependencies

    Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).

  2. 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. 3

    Discover and inject tools

    Call mcpClient.listTools() and spread the result into your agent's tools object. All Jiguang Aurora / 极光 tools become native Mastra tools.

  4. 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: "jiguang-aurora-mcp-client",
  servers: {
    "jiguang-aurora-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Jiguang Aurora / 极光 transactions"
);
console.log(result.text);

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.

Why Choose Vinkius

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

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 Mastra AI

You register the server URL within your Mastra agent configuration. The framework then auto-detects the tools for use in your step definitions.
You can set requireToolApproval for specific actions. This forces a human check before the agent actually fires a message to a user.
Yes, you can branch your workflow based on the tool result. If `get_push_report` shows a low delivery rate, the agent can trigger an alert.
It takes one command to add the client. You then spread the tools into your agent definition and you are ready to go.
The server operates in an ephemeral sandbox. It only touches your device registration IDs and notification content during the active agent session.

Start using the Jiguang Aurora / 极光 MCP today

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