How to Use the YesNo MCP in AutoGen

Q: Can I use YesNo in AutoGen to simulate conflict?

Yes. By using getdecision, you introduce a variable outcome that forces multiple agents to debate and negotiate toward a consensus.

Q: How do I control the outcome when using YesNo with AutoGen?

You can optionally force an answer (e.g., 'yes') via getdecision. This sets the initial premise that all debating agents must then address.

Let AutoGen debate: Use the YesNo MCP Server to drive consensus-driven outcomes.

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Connect YesNo MCP to AutoGen

Create your Vinkius account to connect YesNo 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 YesNo with AutoGen

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Driving Consensus with MCP Server Decisions

The `get_decision` tool introduces a random yes, no, or maybe result into your multi-agent conversation. This forces agents to debate and argue over an uncertain outcome. The decision output serves as the critical point of contention that requires deliberation among competing perspectives.

Testing Argumentation Paths in AutoGen

Want to see how your security agent reacts to uncertainty? Use `get_decision` and force a specific answer. The agents must then discuss why that forced outcome is valid or risky. This capability lets you test the full range of your multi-agent system's negotiation skills.

Handling Ambiguity in Agent Debates

If a decision point is genuinely unclear, `get_decision` provides 'maybe'. This option gives agents something concrete—even if uncertain—to debate. The conversation moves forward by requiring consensus on the ambiguous outcome.

Setup guide

Set up YesNo 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 YesNo 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="YesNo_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about YesNo MCP in AutoGen

Yes. By using `get_decision`, you introduce a variable outcome that forces multiple agents to debate and negotiate toward a consensus.

Yes. The decision (yes/no/maybe) is passed as a clear, definable message input that all agents in the conversation must process and respond to.

You can optionally force an answer (e.g., 'yes') via `get_decision`. This sets the initial premise that all debating agents must then address.

The server supports both stdio and Streamable HTTP transports, allowing it to integrate into various agent conversation flows.

This server touches non-sensitive decision metadata—a randomized yes/no/maybe choice and its associated GIF. The core data type is a simple decision string.

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