How to Use the Volcengine RTC MCP in Mastra AI

Q: What sequence can I use for Volcengine RTC with Mastra AI?

A reliable sequence includes: 1) Check available rooms (getactiverooms). 2) Identify target users (getroomusers). 3) Execute moderation (kickuser or mutestream). The workflow handles retries if any step fails.

Build reliable, multi-step workflows using Volcengine RTC management with Mastra AI.

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Connect Volcengine RTC MCP to Mastra AI

Create your Vinkius account to connect Volcengine RTC 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 Volcengine RTC with Mastra AI

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Managing Participant Lists

Need to know who's in a call? Use `get_room_users` to fetch the full roster for any given room. You can combine this with `get_active_rooms` first to pinpoint the right space. This lets your workflow check membership before attempting any moderation action.

Controlling Audio/Video Feeds

You can programmatically mute or unmute specific streams. Calling `mute_stream` on 'video' stops the visual feed instantly, while calling `unmute_stream` brings it back. This is useful for building complex moderation steps that require fine-grained control over media outputs.

Cloud Media Pipeline Control

The workflow engine manages the entire recording lifecycle. Start the capture process with `start_cloud_record`, and when all data is gathered, trigger `stop_cloud_record`. If you need to format or adapt a stream mid-process, initiate transcode using `start_transcode`.

Setup guide

Set up Volcengine RTC 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 Volcengine RTC 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: "volcengine-rtc-mcp-client",
  servers: {
    "volcengine-rtc-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Volcengine RTC transactions"
);
console.log(result.text);

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Common questions about Volcengine RTC MCP in Mastra AI

Mastra's built-in workflow engine supports complex branching. For example, if the user list is empty after checking rooms via `get_room_users`, the process can automatically retry or notify an admin.

Yes. You build workflows that treat resource management as steps. One step might be listing rooms using `get_active_rooms`, followed by another step acting on the resulting data.

A reliable sequence includes: 1) Check available rooms (`get_active_rooms`). 2) Identify target users (`get_room_users`). 3) Execute moderation (`kick_user` or `mute_stream`). The workflow handles retries if any step fails.

It does. You get automatic retries with exponential backoff, which is critical when dealing with external services like cloud recording or transcoding endpoints.

This server manages real-time communication metadata. Specifically, it handles user identifiers from rooms and the operational status of media streams (audio/video).

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