How to Use the Fairing MCP in AutoGen

Q: Can AutoGen agents share Fairing data during a debate?

Yes. Once an agent calls a tool like getinsights, the results are shared within the conversation. Other agents can then reference this data to support their arguments.

Debate customer feedback with AutoGen agents backed by real-time Fairing data.

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Connect Fairing MCP to AutoGen

Create your Vinkius account to connect Fairing 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 Fairing with AutoGen

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Perplexity

Enable agent debate using Fairing tools

Set up multiple agents to interpret your survey data differently. One agent calls `get_insights` to identify trends, while another challenges those findings with raw data from `list_responses`. They reach a consensus based on the actual survey records. This removes bias from your data analysis by forcing agents to argue for their conclusions.

Automate Fairing insight gathering in AutoGen

Assign an agent to monitor your account status using `get_me` and `get_account_info`. It keeps the team updated on survey health without human intervention. If the data indicates a change in customer sentiment, the agent flags it for the rest of the group. It acts as a persistent analyst that never sleeps.

Negotiate survey strategies with Fairing MCP

Build a system where agents discuss which surveys to prioritize. They use `list_surveys` to scan your active campaigns and decide which ones need more attention. This is about finding the best path forward through deliberation. The agents negotiate based on the specific response metrics they pull from the server.

Setup guide

Set up Fairing 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 Fairing 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="Fairing_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Fairing MCP in AutoGen

Yes. Once an agent calls a tool like `get_insights`, the results are shared within the conversation. Other agents can then reference this data to support their arguments.

You pass the MCP tools to your agent constructors. The adapter handles the schema mapping, allowing your agents to invoke the Fairing functions directly in their chat.

Yes, it stays within your Vinkius-managed session. You define the agent's capabilities, so it only accesses the specific survey information it needs for its assigned task.

It allows for multi-perspective analysis. Instead of one agent looking at a single number, your agents can debate the context behind the zero-party data to find the real meaning.

It is designed to be efficient. You can query your survey records as needed during the agent's deliberation process, provided you stay within the API rate limits.

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