How to Use the ZEGO / 即构科技 MCP in LlamaIndex

Q: Can I query historical room data using ZEGO / 即构科技 and LlamaIndex?

Yes. You index the outputs from tools like getroomusers or listrooms. This lets you build a knowledge graph that answers questions about past room compositions, not just current ones.

Q: How do I manage users with ZEGO / 即构科技 and LlamaIndex?

You run tools like checkuserstatus. The output—the user's current status—is then saved into the index. This gives you a long-term, searchable record of their activity.

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Connect ZEGO / 即构科技 MCP to LlamaIndex

Create your Vinkius account to connect ZEGO / 即构科技 to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup ZEGO / 即构科技 with LlamaIndex

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Indexing Room Data with MCP Server

You can use `list_rooms` to get a list of all active room names. By running this tool, the output is indexed into your vector store. This allows users to query not just live data, but also historical knowledge about what kinds of rooms existed previously. The resulting index lets you ask questions like, 'What were our most commonly used rooms last month?' and get answers grounded in past API calls.

Analyzing Service Usage Statistics with LlamaIndex

Run `get_usage_stats` to capture the service metrics for a given time period. Indexing this data means your RAG application can retrieve specific performance details—like bandwidth usage or active user counts—months later. It turns raw API numbers into searchable facts, letting you build compliance reports or post-mortem analyses without needing a separate database connection.

Tracking User Activity via MCP Server

If you run `check_user_status` for several users, the resulting list of statuses (online, offline, etc.) can be indexed. This creates an archive of user presence that's far richer than a simple database entry. The index helps your team query past sessions: 'When did User X last appear online?' and get a detailed answer based on historical tool outputs.

Setup guide

Set up ZEGO / 即构科技 MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all ZEGO / 即构科技 MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to ZEGO / 即构科技 tools.",
)
response = await agent.run("List recent ZEGO / 即构科技 data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ZEGO / 即构科技. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about ZEGO / 即构科技 MCP in LlamaIndex

The MCP Server output serves as the source material for your knowledge base. You run a tool like `get_room_users`, and the resulting list of user IDs is immediately indexed, making it searchable alongside your documents.

Yes. You index the outputs from tools like `get_room_users` or `list_rooms`. This lets you build a knowledge graph that answers questions about past room compositions, not just current ones.

It touches operational metadata: user lists, stream identifiers, usage statistics, and overall connectivity counts. This is all rich, structured data perfect for indexing into a vector store.

It allows you to combine the Zego API with other external knowledge sources. You can cross-reference 'Who was online?' (from the MCP Server) with 'What project were they working on?' (from a document store).

You run tools like `check_user_status`. The output—the user's current status—is then saved into the index. This gives you a long-term, searchable record of their activity.

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