How to Use the Netease Yunxin / 网易云信 MCP in Mastra AI

Q: How does Mastra AI send messages through Netease Yunxin / 网易云信?

Your agent calls the sendp2pmessage tool for one-to-one chats or sendbatchmessage for broadcasts. Mastra AI's workflow engine ensures the call is made, and you can build in retry logic for guaranteed delivery.

Q: Can I build a user management workflow for Netease Yunxin / 网易云信 with Mastra AI?

Absolutely. You can chain tools like createaccount, updateaccount, and addteammember into a single, robust workflow. Mastra AI manages the state and execution between each step.

Q: How does Mastra AI handle fetching team details from Netease Yunxin / 网易云信?

Your agent simply calls the getteamdetail tool. The MCP server returns the information, which you can then use as input for the next step in your Mastra AI workflow, like sending a summary to an admin.

Q: What if a user's IM token expires?

This server includes a refreshusertoken tool. You can build a scheduled Mastra AI workflow that periodically checks for and refreshes tokens, ensuring your users' sessions remain active without any manual intervention.

Build failure-proof chat and user management workflows for Netease Yunxin with Mastra AI's engine.

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Connect Netease Yunxin / 网易云信 MCP to Mastra AI

Create your Vinkius account to connect Netease Yunxin / 网易云信 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 Netease Yunxin / 网易云信 with Mastra AI

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Automate User Onboarding

This server provides the tools to build a complete, automated onboarding sequence. Your Mastra AI agent can use a workflow that first calls `create_account` to provision a new user in Netease Yunxin. It's a single, reliable step. Then, you add conditional logic. If the account creation succeeds, the workflow proceeds to `add_team_member` to place them in a welcome channel. If it fails, Mastra's engine can automatically retry the call or trigger a different workflow to notify an admin. You build for success and failure at the same time.

Reliable Team Administration Workflows

Use Mastra AI to build complex, multi-step administrative tasks that just work. For example, a moderation workflow can be triggered by an external event. The agent then calls `kick_team_member` to remove a user from a specific Netease Yunxin group. Because Mastra AI waits for each step to complete, you can chain actions with confidence. After kicking the member, the next step in the workflow can call `get_team_detail` to verify the user is gone and then use `send_p2p_message` to log the action to an admin channel. This MCP Server makes it deterministic.

Guaranteed Batch Messaging

When you need to send an important announcement, you can't afford to have it fail. The `send_batch_message` tool lets your agent push a message to a large list of Netease Yunxin users at once. Here's the thing: network requests can fail. Mastra AI's built-in support for retries with exponential backoff means your agent won't just give up. If the initial call to this MCP server fails, Mastra will try again automatically, ensuring your critical communications get delivered.

Setup guide

Set up Netease Yunxin / 网易云信 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 Netease Yunxin / 网易云信 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: "netease-yunxin-mcp-client",
  servers: {
    "netease-yunxin-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Netease Yunxin / 网易云信 Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Netease Yunxin / 网易云信 tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Netease Yunxin / 网易云信 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 Netease Yunxin / 网易云信. 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 Netease Yunxin / 网易云信 MCP in Mastra AI

Your agent calls the `send_p2p_message` tool for one-to-one chats or `send_batch_message` for broadcasts. Mastra AI's workflow engine ensures the call is made, and you can build in retry logic for guaranteed delivery.

Absolutely. You can chain tools like `create_account`, `update_account`, and `add_team_member` into a single, robust workflow. Mastra AI manages the state and execution between each step.

Your agent simply calls the `get_team_detail` tool. The MCP server returns the information, which you can then use as input for the next step in your Mastra AI workflow, like sending a summary to an admin.

This server includes a `refresh_user_token` tool. You can build a scheduled Mastra AI workflow that periodically checks for and refreshes tokens, ensuring your users' sessions remain active without any manual intervention.

This MCP server processes Netease Yunxin account credentials, team member lists, and message payloads. Communication is encrypted via your Vinkius transport URL, and no persistent data is kept on the server after your Mastra AI workflow completes.

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