How to Use the Zhumu / 瞩目 MCP in Mastra AI

Q: How does Mastra AI handle Zhumu / 瞩目 meeting scheduling?

Mastra AI uses the createmeeting tool, which can be wrapped in a conditional step. If the initial booking fails, the workflow automatically retries with different parameters.

Q: Can Mastra AI access user data from Zhumu / 瞩目?

Yes, it uses listusers and then targets specific accounts via getuser. This allows the agent to build multi-step workflows that depend on accurate directory information.

Q: What is required for Zhumu / 瞩目 account reporting with Mastra AI?

You simply need to call getaccountreport. The tool provides usage metrics, which the agent can then analyze and use as a decision point in its workflow (e.g., if report > X, notify admin).

Q: Does Mastra AI support listing Zhumu / 瞩目 webinars?

Yes, listwebinars is available. The agent can iterate over the list of scheduled events and pass those details to a subsequent action.

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Connect Zhumu / 瞩目 MCP to Mastra AI

Create your Vinkius account to connect Zhumu / 瞩目 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 Zhumu / 瞩目 with Mastra AI

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Automate meeting creation and updates.

You can call `create_meeting` to schedule a new event, or use `update_meeting` if the details change. This is useful for building complex workflows that manage scheduling changes reliably. If you need to clean up old data, the agent can execute `delete_meeting`. The system handles retries if the initial call fails.

Verify user roles and account health.

The workflow starts by calling `list_users` to verify who needs access. Then, it uses `get_user` on specific IDs for validation checks. To confirm permissions or quota limits, the agent runs `get_account_report`. This ensures the subsequent actions in your flow won't fail due to insufficient resources.

Retrieve meeting and content details.

The `list_meetings` tool pulls all upcoming sessions into a list. You can then use `get_meeting` on an item from that list to get deep details. For archived material, the agent uses `list_recordings` or `list_webinars`, making it easy to build pipelines that manage content lifecycle.

Setup guide

Set up Zhumu / 瞩目 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 Zhumu / 瞩目 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: "zhumu-mcp-client",
  servers: {
    "zhumu-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Zhumu / 瞩目 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 Zhumu / 瞩目. 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 Zhumu / 瞩目 MCP in Mastra AI

Mastra AI uses the `create_meeting` tool, which can be wrapped in a conditional step. If the initial booking fails, the workflow automatically retries with different parameters.

Yes, it uses `list_users` and then targets specific accounts via `get_user`. This allows the agent to build multi-step workflows that depend on accurate directory information.

You simply need to call `get_account_report`. The tool provides usage metrics, which the agent can then analyze and use as a decision point in its workflow (e.g., if report > X, notify admin).

Yes, `list_webinars` is available. The agent can iterate over the list of scheduled events and pass those details to a subsequent action.

The server handles meeting settings, user profiles, account reports, recordings, and webinar schedules. The workflow engine manages the flow between these different data types.

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