How to Use the Refiner MCP in AutoGen

Let your AutoGen agents debate user feedback using the Refiner MCP Server.

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Connect Refiner MCP to AutoGen

Create your Vinkius account to connect Refiner 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 Refiner with AutoGen

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Debate feedback using Refiner tools

Assign `list_refiner_responses` to an agent who challenges the team on product direction. Other agents then weigh in on whether the feedback warrants a change. Use `list_refiner_surveys` so the agents understand the context of the feedback. They negotiate based on the actual survey scope.

Manage user updates in AutoGen

Have a specialized agent call `identify_refiner_user` when the group reaches a consensus. This ensures your records stay updated after a team decision. Use `list_refiner_segments` to provide the agents with current user data. They debate how to handle specific segments based on this input.

Automate event tracking with debate

Trigger `track_refiner_event` only after your agents agree on the action. This prevents conflicting data from hitting your logs. Keep the pipeline stable by checking `check_refiner_status` before any agent takes action. If the system is offline, the agents discuss an alternative path.

Setup guide

Set up Refiner 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 Refiner 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="Refiner_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about Refiner MCP in AutoGen

The McpToolAdapter automatically maps the tools to your agents. You pass the list to the AssistantAgent, and it decides when to call them during the conversation.

Yes, by providing real user feedback as evidence. Agents use this data to back up their arguments during the debate process.

It helps, but isn't required. Using a group chat allows agents to challenge the data retrieved from your survey responses.

Agents pass tool outputs back and forth in their conversation history. Every participant sees the results of the last tool call.

Your survey responses and contact records are handled within our V8 sandbox. Each request uses a single endpoint token to ensure only your authorized agents see the data.

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