How to Use the AntEater MCP in AutoGen

Enable multi-agent debate in AutoGen using real-time AntEater team activity updates.

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Connect AntEater MCP to AutoGen

Create your Vinkius account to connect AntEater 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 AntEater with AutoGen

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Debate AntEater insights with AutoGen

Let a security agent and a performance agent negotiate based on `search_activity` results. They compare findings until they reach a consensus. This removes bias from your data analysis. You get a vetted conclusion rather than a single, potentially flawed perspective.

AutoGen MCP Server consensus

The agents use `get_user` to gather facts before they argue. They challenge each other's interpretation of team member roles and history. This ensures that every decision the system makes is challenged. It is built for complex scenarios where the right answer is not immediately obvious.

Automated AntEater task execution

Configure the AssistantAgent to trigger `check_anteater_status` when the debate concludes. The system verifies connectivity before it finishes the task. This creates a self-correcting loop. If the connection fails, the agents re-evaluate and try a different approach.

Setup guide

Set up AntEater 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 AntEater 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="AntEater_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about AntEater MCP in AutoGen

They use the adapter to pull the tool schema. Once loaded, the agents treat these tools as native functions during their conversation.

Yes, by providing the data agents use to build their arguments. The tool output acts as the ground truth for their discussion.

It works perfectly with any configuration. Each agent can have specific tool access depending on its assigned role.

You can inspect the conversation history to see which agent called which tool. The entire deliberation process is logged for review.

Your activity logs are transmitted over an encrypted, ephemeral channel. We rotate your session tokens frequently to ensure zero-trust access.

Use it with your favorite AI tools

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

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Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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