How to Use the Aporia MCP in AutoGen

Q: How do I expose Aporia tools to AutoGen agents?

Install autogen-extmcp. Use mcpservertools with your HTTP server parameters, and pass the resulting tools list directly to your AssistantAgent constructor.

Q: Can multiple AutoGen agents use Aporia simultaneously?

Yes. The McpToolAdapter handles schema conversion so any agent in the conversation can invoke validateguardrails or getmetrics if they have the tools assigned.

Q: How does AutoGen handle failed Aporia monitor triggers?

If triggermonitor returns an error, the agent receives the failure context. It then debates with other agents to retry the call or escalate the issue to a human supervisor.

Give your AutoGen agents the power to debate safety and trigger Aporia guardrails autonomously.

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Setup Aporia with AutoGen

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AutoGen agents debating safety

You don't want a single agent making unilateral decisions about model toxicity. With AutoGen, you assign `validate_guardrails` to a dedicated security agent. When the primary agent drafts a response, the security agent intercepts it and runs the check. If the check fails, they negotiate. The security agent flags the PII violation, and the writing agent revises the text. They iterate until the output passes the Aporia rules before releasing it to the user.

Automated incident response via MCP Server

When things break, human triage takes too long. If an agent notices severe drift in a downstream task, it immediately calls `trigger_monitor`. A separate observability agent then calls `list_monitors` to verify the execution. This creates a self-healing diagnostic loop. The agents discuss the anomaly, pull the latest data using `get_metrics`, and formulate a mitigation plan. You get consensus-driven incident response without waking up an engineer.

Audit your model configurations

Configuration drift happens when teams manually tweak settings. You deploy an auditor agent to periodically run `list_models` and `get_model`. It checks the active state of your workspace against your documented baseline. If it finds a discrepancy, it alerts a manager agent. They review the available dashboards via `list_dashboards` and debate whether the changes violate compliance policies.

Setup guide

Set up Aporia 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 Aporia 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="Aporia_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Aporia. 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 Aporia MCP in AutoGen

Install `autogen-ext[mcp]`. Use `mcp_server_tools` with your HTTP server parameters, and pass the resulting tools list directly to your `AssistantAgent` constructor.

Yes. The `McpToolAdapter` handles schema conversion so any agent in the conversation can invoke `validate_guardrails` or `get_metrics` if they have the tools assigned.

If `trigger_monitor` returns an error, the agent receives the failure context. It then debates with other agents to retry the call or escalate the issue to a human supervisor.

No. AutoGen automatically translates the MCP protocol definitions into native tool schemas. The agents understand exactly what arguments `list_monitors` expects.

The server reads your workspace configurations and model IDs strictly over an encrypted connection. The Vinkius infrastructure ensures that once the agent session closes, all traces of your `get_model` requests are destroyed instantly.

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