ZEGO / 即构科技 MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ZEGO / 即构科技 as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 ZEGO / 即构科技. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in ZEGO / 即构科技?"
)
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 ZEGO / 即构科技 MCP Server
Empower your AI agent to orchestrate your real-time communication infrastructure with ZEGO (即构科技), the premier provider of global video and audio RTC services. By connecting ZEGO to your agent, you transform complex room management, stream control, and user status tracking into a natural conversation. Your agent can instantly retrieve active room lists, monitor user counts, force-stop media streams, and audit service usage statistics without you ever needing to navigate multiple technical dashboards. Whether you are building an automated moderation system for live rooms or monitoring cross-regional connectivity, your agent acts as a real-time RTC operations assistant, providing accurate and reliable results from a single, authorized source.
LlamaIndex agents combine ZEGO / 即构科技 tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through 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
- Room Orchestration — List active rooms, retrieve detailed metadata, and monitor real-time user activity.
- User Management — Track user status (online/offline), list members in specific rooms, and manage access (kick users).
- Stream Control — Monitor active media streams and force-terminate unauthorized or problematic broadcasts.
- Usage Auditing — Retrieve comprehensive audio and video duration statistics for specific time ranges.
- Operational Insights — Monitor total online user counts and API connectivity status to ensure system-wide health.
The ZEGO / 即构科技 MCP Server exposes 8 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 ZEGO / 即构科技 to LlamaIndex via MCP
Follow these steps to integrate the ZEGO / 即构科技 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 8 tools from ZEGO / 即构科技
Why Use LlamaIndex with the ZEGO / 即构科技 MCP Server
LlamaIndex provides unique advantages when paired with ZEGO / 即构科技 through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ZEGO / 即构科技 tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ZEGO / 即构科技 tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ZEGO / 即构科技, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ZEGO / 即构科技 tools were called, what data was returned, and how it influenced the final answer
ZEGO / 即构科技 + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ZEGO / 即构科技 MCP Server delivers measurable value.
Hybrid search: combine ZEGO / 即构科技 real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ZEGO / 即构科技 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 ZEGO / 即构科技 for fresh data
Analytical workflows: chain ZEGO / 即构科技 queries with LlamaIndex's data connectors to build multi-source analytical reports
ZEGO / 即构科技 MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect ZEGO / 即构科技 to LlamaIndex via MCP:
check_user_status
Check status of multiple users
get_online_count
Get total online user count
get_room_streams
List active streams in a room
get_room_users
List users in a room
get_usage_stats
Get service usage statistics
kick_room_user
Kick user from room
list_rooms
List active rooms
stop_media_stream
Force stop a stream
Example Prompts for ZEGO / 即构科技 in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ZEGO / 即构科技 immediately.
"List all active rooms in our ZEGO project."
"Check the status for these users: 'user_01,user_02'."
"What is our video usage duration for the last 7 days?"
Troubleshooting ZEGO / 即构科技 MCP Server with LlamaIndex
Common issues when connecting ZEGO / 即构科技 to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZEGO / 即构科技 + LlamaIndex FAQ
Common questions about integrating ZEGO / 即构科技 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 ZEGO / 即构科技 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 ZEGO / 即构科技 to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.