Zhumu / 瞩目 MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Zhumu / 瞩目 through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Zhumu / 瞩目 Assistant",
instructions=(
"You help users interact with Zhumu / 瞩目. "
"You have access to 10 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Zhumu / 瞩目"
)
print(result.final_output)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
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.
The OpenAI Agents SDK auto-discovers all 10 tools from Zhumu / 瞩目 through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Zhumu / 瞩目, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.
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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP
Follow these steps to integrate the Zhumu / 瞩目 MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 10 tools from Zhumu / 瞩目
Why Use OpenAI Agents SDK with the Zhumu / 瞩目 MCP Server
OpenAI Agents SDK provides unique advantages when paired with Zhumu / 瞩目 through the Model Context Protocol.
Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Zhumu / 瞩目 + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Zhumu / 瞩目 MCP Server delivers measurable value.
Automated workflows: build agents that query Zhumu / 瞩目, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents — one queries Zhumu / 瞩目, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Zhumu / 瞩目 tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Zhumu / 瞩目 to resolve tickets, look up records, and update statuses without human intervention
Zhumu / 瞩目 MCP Tools for OpenAI Agents SDK (10)
These 10 tools become available when you connect Zhumu / 瞩目 to OpenAI Agents SDK 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 OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK
Common issues when connecting Zhumu / 瞩目 to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Zhumu / 瞩目 + OpenAI Agents SDK FAQ
Common questions about integrating Zhumu / 瞩目 MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Zhumu / 瞩目 with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Zhumu / 瞩目 to OpenAI Agents SDK
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.