Zhumu / 瞩目 MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Zhumu / 瞩目 through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"zhumu": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Zhumu / 瞩目, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Zhumu / 瞩目 through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Zhumu / 瞩目 MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from Zhumu / 瞩目 via MCP
Why Use LangChain with the Zhumu / 瞩目 MCP Server
LangChain provides unique advantages when paired with Zhumu / 瞩目 through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Zhumu / 瞩目 MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Zhumu / 瞩目 queries for multi-turn workflows
Zhumu / 瞩目 + LangChain Use Cases
Practical scenarios where LangChain combined with the Zhumu / 瞩目 MCP Server delivers measurable value.
RAG with live data: combine Zhumu / 瞩目 tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Zhumu / 瞩目, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Zhumu / 瞩目 tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Zhumu / 瞩目 tool call, measure latency, and optimize your agent's performance
Zhumu / 瞩目 MCP Tools for LangChain (10)
These 10 tools become available when you connect Zhumu / 瞩目 to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Zhumu / 瞩目 to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersZhumu / 瞩目 + LangChain FAQ
Common questions about integrating Zhumu / 瞩目 MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.