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

Achieve Consensus Decisions with AutoGen

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Connect ZEGO / 即构科技 MCP to AutoGen

Create your Vinkius account to connect ZEGO / 即构科技 to AutoGen 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 AutoGen

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Automated Moderation in AutoGen

Imagine a system needing to manage user behavior. One agent can run `get_room_users` and identify users whose status is suspicious via `check_user_status`. A second, policy-focused agent then debates whether the risk warrants an immediate action. The agents will converge on a decision—perhaps running `kick_room_user` only if two different agents agree the user violated policy.

Performance Analysis with AutoGen and MCP Server

You can set up agents to argue about resource allocation. One agent pulls data from `get_usage_stats`, pointing out high bandwidth usage. A second, optimization agent then checks the total online count using `get_online_count`. The debate leads to a recommendation: 'We need to cap streaming quality for all rooms'—a decision derived from analyzing multiple metrics.

Complex Stream Control with AutoGen

A multi-agent setup can manage media streams. One agent lists available streams using `get_room_streams`. A second, governance agent then checks the user count (`get_room_users`) to ensure that stopping a stream is necessary and justified. The process models human deliberation, ensuring critical actions like calling `stop_media_stream` only happen when consensus is reached.

Setup guide

Set up ZEGO / 即构科技 MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes ZEGO / 即构科技 tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="ZEGO / 即构科技_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent ZEGO / 即构科技 data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="ZEGO / 即构科技_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent ZEGO / 即构科技 data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

The MCP Server provides the raw, live data that fuels the agents' debate. The tools are treated as facts the agents must argue over, leading to a consensus decision.

Yes. You build systems where the answer isn't obvious. For example, an agent might need to check `get_room_users` first, then call `check_user_status`, before making a decision about kicking someone.

It handles live, operational telemetry: user lists, room IDs, stream identifiers, and usage metrics. These are the 'facts' that the debating agents use to reach a conclusion.

The framework supports multiple servers by aggregating tool definitions. This means your agent can pull data from Zego's communications platform and cross-reference it with other services.

You can build systems that automate complex governance. Instead of a single script, the agents debate: 'Should we kick this user? Agent A says yes based on usage; Agent B says no because they just logged in.'

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