How do I find my Zhumu App Key and Secret?

Log in to the Zhumu Developer Center, create a new application, and you will find your unique App Key and App Secret in the application credentials section.

Can I retrieve meeting recordings through this server?

Yes. Use the `list_recordings` tool with a specific user ID to retrieve a list of all cloud recordings saved for that user's sessions.

Is it possible to manage webinars?

Yes! Use the `list_webinars` tool to retrieve a list of all scheduled webinars and identify upcoming sessions and engagement metrics.

Zhumu / 瞩目 MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Zhumu / 瞩目
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Zhumu / 瞩目 through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Zhumu / 瞩目 "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Zhumu / 瞩目?"
    )
    print(result.data)

asyncio.run(main())

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About Zhumu / 瞩目 MCP Server

Empower your AI agent to orchestrate your video collaboration with Zhumu (瞩目), the premier cloud meeting platform in China. By connecting Zhumu to your agent, you transform complex meeting scheduling, user auditing, and recording management into a natural conversation. Your agent can instantly list upcoming meetings, retrieve detailed participant information, monitor cloud recordings, and even schedule new sessions without you ever needing to navigate the comprehensive Zhumu portal. Whether you are conducting a cross-functional team sync or coordinating a large-scale webinar, your agent acts as a real-time collaboration assistant, keeping your schedule accurate and your meetings organized.

Pydantic AI validates every Zhumu / 瞩目 tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Meeting Orchestration — List, retrieve, and schedule video meetings with full support for topics and timing.
User Auditing — Browse and retrieve detailed user profiles across your organization.
Recording Control — List and access cloud recordings for past sessions to ensure knowledge sharing.
Webinar Monitoring — Monitor scheduled webinars and participant engagement levels.
Usage Insights — Retrieve high-level account reports and activity summaries for your collaboration environment.

The Zhumu / 瞩目 MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Zhumu / 瞩目 to Pydantic AI via MCP

Follow these steps to integrate the Zhumu / 瞩目 MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Zhumu / 瞩目 with type-safe schemas

Why Use Pydantic AI with the Zhumu / 瞩目 MCP Server

Pydantic AI provides unique advantages when paired with Zhumu / 瞩目 through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Zhumu / 瞩目 integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Zhumu / 瞩目 connection logic from agent behavior for testable, maintainable code

Zhumu / 瞩目 + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Zhumu / 瞩目 MCP Server delivers measurable value.

Type-safe data pipelines: query Zhumu / 瞩目 with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Zhumu / 瞩目 tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Zhumu / 瞩目 and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Zhumu / 瞩目 responses and write comprehensive agent tests

Zhumu / 瞩目 MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Zhumu / 瞩目 to Pydantic AI via MCP:

create_meeting

Create a new meeting

delete_meeting

Delete a meeting

get_account_report

Get usage reports

get_meeting

Get meeting details

get_user

Get user details

list_meetings

List upcoming meetings

list_recordings

List cloud recordings

list_users

List organization users

list_webinars

List scheduled webinars

update_meeting

Update meeting settings

Example Prompts for Zhumu / 瞩目 in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Zhumu / 瞩目 immediately.

"List all my upcoming meetings in Zhumu."

"Schedule a meeting titled 'Design Feedback' for today."

"Show me the last 5 cloud recordings."

Troubleshooting Zhumu / 瞩目 MCP Server with Pydantic AI

Common issues when connecting Zhumu / 瞩目 to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Zhumu / 瞩目 + Pydantic AI FAQ

Common questions about integrating Zhumu / 瞩目 MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Zhumu / 瞩目 MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.