How to Use the Zenloop MCP in AutoGen

Q: Can AutoGen manage multiple Zenloop surveys?

Yes. Agents can run multi-step processes: first calling listsurveys, and then sequentially passing those survey IDs into getsurveydetails for individual analysis.

Run multi-agent debates on Zenloop data using AutoGen's conversation framework.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Zenloop MCP to AutoGen

Create your Vinkius account to connect Zenloop 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.

GDPR Free for Subscribers

Setup Zenloop with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate Feedback Management

Use the `create_email_answer`, `create_embed_answer`, and other creation tools to simulate a debate on best practices. Agents can discuss which channel—like Website Embed or Link—is most appropriate for submitting new survey responses.

Collaborative Data Gathering

Multiple agents can work together to gather necessary context. One agent might use `list_surveys` while another uses `get_survey_details`. They then negotiate which data points are needed for the final conclusion.

Structured Information Retrieval

The tools `get_account_details`, `get_survey_details`, and `list_survey_answers` allow agents to debate the best approach to data retrieval. For example, one agent insists on filtering by date using `list_survey_answers`, while another suggests getting all available details first.

Setup guide

Set up Zenloop 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 Zenloop 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="Zenloop_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Zenloop_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zenloop 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 Zenloop. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Zenloop now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Zenloop MCP in AutoGen

The agents receive the MCP Server tools list. When a decision is made, an agent selects and calls the required function—say, `list_surveys`—and uses its output to inform the conversation.

Yes. Agents can run multi-step processes: first calling `list_surveys`, and then sequentially passing those survey IDs into `get_survey_details` for individual analysis.

The server provides eight functions, including creating responses (`create_*_answer`) and listing/retrieving all types of survey information.

While the MCP Server is stateless by default, AutoGen's framework manages the conversation history, providing a robust conversational memory that helps agents maintain context across multiple tool calls.

This server handles customer survey responses and account details. The raw data points managed are the contents returned by `list_survey_answers`.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python