How to Use the Medallia MCP in AutoGen

Deploy a debate-ready team in AutoGen to negotiate customer experience outcomes using Medallia data.

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Connect Medallia MCP to AutoGen

Create your Vinkius account to connect Medallia 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 Medallia with AutoGen

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Claude

Perplexity

Negotiate alerts with multi-agent debate

Let your security agent flag risks in `list_alerts` while a performance agent argues for faster resolution. They debate the priority of each issue until they reach a consensus. This moves your team past simple status checks. You get a decision-making process that accounts for competing business needs.

Automate user access and survey reviews

Assign one agent to `list_users` and another to `get_survey` for cross-checking permissions and feedback quality. They verify that the right people see the right data. It removes the need for manual auditing. The agents handle the verification work while you focus on the final report.

Converge on response insights

Use `get_response` to feed feedback into an agent conversation. Agents challenge each other's interpretations of the text to ensure the final summary is accurate. It eliminates individual bias in your reporting. You get a nuanced view that represents multiple angles of the customer experience.

Setup guide

Set up Medallia 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 Medallia 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="Medallia_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about Medallia MCP in AutoGen

You pass the tool list to the AssistantAgent constructor. All agents in your group chat then have access to the same Medallia functions.

Yes, that is the point of the debate framework. They will compare `list_surveys` results and discuss discrepancies until they agree on a conclusion.

You should add a custom coordinator agent that monitors call frequency. It can throttle requests if the agents get too aggressive with polling.

The data is kept within the ephemeral sandbox of the server. It is never stored or shared outside your active session.

No, the adapter handles schema conversion automatically. You just define the agents and they handle the tool execution flow.

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

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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