Zhumu / 瞩目 MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Zhumu / 瞩目 as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Zhumu / 瞩目. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Zhumu / 瞩目?"
)
print(response)
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.
LlamaIndex agents combine Zhumu / 瞩目 tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Zhumu / 瞩目 MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Zhumu / 瞩目 MCP Server
LlamaIndex provides unique advantages when paired with Zhumu / 瞩目 through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Zhumu / 瞩目 tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Zhumu / 瞩目 tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Zhumu / 瞩目, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Zhumu / 瞩目 tools were called, what data was returned, and how it influenced the final answer
Zhumu / 瞩目 + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Zhumu / 瞩目 MCP Server delivers measurable value.
Hybrid search: combine Zhumu / 瞩目 real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Zhumu / 瞩目 to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Zhumu / 瞩目 for fresh data
Analytical workflows: chain Zhumu / 瞩目 queries with LlamaIndex's data connectors to build multi-source analytical reports
Zhumu / 瞩目 MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Zhumu / 瞩目 to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Zhumu / 瞩目 to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZhumu / 瞩目 + LlamaIndex FAQ
Common questions about integrating Zhumu / 瞩目 MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.