How to Use the ZEGO / 即构科技 MCP in Mastra AI

Q: Can I list rooms using ZEGO / 即构科技 with Mastra AI?

Absolutely. The listrooms tool is available for your agent to execute, giving it a defined scope of active communication areas to work within.

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Connect ZEGO / 即构科技 MCP to Mastra AI

Create your Vinkius account to connect ZEGO / 即构科技 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 ZEGO / 即构科技 with Mastra AI

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User Status Checks and Counts

The `check_user_status` tool lets your agent verify the status of multiple users before proceeding. If a user is inactive, your workflow can automatically trigger an alert. The `get_online_count` function provides the total number of connected users. You'll use this in conditional logic—for instance, 'if online count drops below 10, notify admin.' This handles critical operational checks.

Room and Media Management

Your agent can start by running `list_rooms` to map out all active communication areas. From there, it uses `get_room_users` to confirm who is present in a target room. Need to control media? The `stop_media_stream` tool forces streams off and the `get_room_streams` tool lists what's currently playing.

Moderation and Usage Tracking

If a user causes trouble, the workflow can execute `kick_room_user` to remove them. This is an immediate action that needs reliable retries. The system also supports tracking performance via `get_usage_stats`. You'll use this metric data inside your workflow engine for compliance or billing checks.

Setup guide

Set up ZEGO / 即构科技 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 ZEGO / 即构科技 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: "zego-mcp-client",
  servers: {
    "zego-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent ZEGO / 即构科技 transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ZEGO / 即构科技. 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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Common questions about ZEGO / 即构科技 MCP in Mastra AI

Mastra handles the complex execution flow. You define a sequence of actions—like checking status, then kicking a user—and it manages retries and failure handling across all those steps.

It manages connection metadata: user IDs, room lists, online counts, and stream status. These are the inputs your complex workflows depend on.

Absolutely. The `list_rooms` tool is available for your agent to execute, giving it a defined scope of active communication areas to work within.

Yes. Since Mastra includes automatic retries and exponential backoff, the `kick_room_user` action is designed to complete even if initial network calls fail.

You can build a workflow that periodically checks service capacity using `get_usage_stats`. This ensures your multi-step process never hits an unexpected limit.

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