How to Use the Zenedu MCP in AutoGen

Q: How does AutoGen use Zenedu for decision making?

AutoGen's agents debate the results from tools like listbotorders. Instead of just reporting data, the conversation converges on a recommendation—for example, 'We should focus marketing efforts on Bot X because its orders are high but funnels are incomplete.'

Achieve Consensus on Bot Strategy with AutoGen and Zenedu MCP Server.

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Connect Zenedu MCP to AutoGen

Create your Vinkius account to connect Zenedu 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 Zenedu with AutoGen

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Assess the full bot asset portfolio

Start by having one agent run `list_bots` to generate a comprehensive list of all existing bots. A second agent can then use that output, calling `list_bot_products`, to immediately identify what commercial assets are tied to each bot. This forces the agents to debate and confirm which products truly belong to which operational bot.

Debate subscriber profitability

Set up a conflict: Agent Alpha pulls all data using `list_bot_subscribers`, while Agent Beta runs `list_bot_orders`. The agents then debate the status of certain users—is their low order count due to poor service, or are they just new subscribers? This consensus-driven approach finds overlooked patterns in user behavior.

Simulate funnel improvements

You can make agents debate optimization strategies. One agent pulls current flow data with `list_bot_funnels`, and another checks available offers using `list_bot_offers`. They then argue whether a specific offer should be inserted into the existing funnel at a certain step. The outcome is a negotiated, improved business process.

Setup guide

Set up Zenedu 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 Zenedu 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="Zenedu_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Zenedu 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="Zenedu_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zenedu data")
    print(result.messages[-1].content)

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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 Zenedu. 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 Zenedu MCP in AutoGen

AutoGen's agents debate the results from tools like `list_bot_orders`. Instead of just reporting data, the conversation converges on a recommendation—for example, 'We should focus marketing efforts on Bot X because its orders are high but funnels are incomplete.'

Yes. The server tools fit perfectly into multi-agent conversations because they provide structured data that different specialized agents can challenge and build upon.

The communication messaging data allows the agents to debate operational choices, such as which bot needs more products added or if a specific subscriber group is underperforming based on order history.

You can. Pass the list of Zenedu tools to your AssistantAgent constructor. This makes all bot management functions available for agents to call and discuss among themselves.

Zenedu manages communication messaging assets, specifically exposing the capability to list bots, products, subscribers, funnels, and orders.

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