How to Use the Trend Micro MCP in AutoGen

Q: What kind of telemetry can be used with Trend Micro and AutoGen?

You feed agents endpoint activity logs (listendpointactivitylogs), recent detections, and detailed security alerts. The data guides the conversation toward a consensus decision.

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Setup Trend Micro with AutoGen

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Perplexity

Debate threat findings using the MCP Server.

One agent can run `list_security_alerts` to flag a potential risk, while another agent uses `get_alert_details` to gather deeper evidence. The system then debates whether or not this finding warrants an immediate high-priority ticket. This consensus model is ideal for security operations. It forces different perspectives—risk vs. efficiency—to negotiate the final action.

Audit multi-asset environments with AutoGen agents.

Multiple agents can work together: one calls `list_managed_endpoints` to build a full asset inventory, while another runs `list_endpoint_activity_logs` against that list. They then debate which assets need the most immediate attention. This multi-agent conversation ensures thorough coverage across your entire estate before making a final recommendation.

Cross-check intelligence with MCP Server.

A threat agent runs `list_suspicious_objects` to gather the latest bad data. A separate policy agent then debates whether existing detection rules need updating based on those new findings. They negotiate a patch or rule change. This structured deliberation is key when dealing with evolving threats, moving beyond simple reporting into actionable system modification.

Setup guide

Set up Trend Micro 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 Trend Micro 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="Trend Micro_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Trend Micro MCP in AutoGen

The multi-agent framework treats the tool output as a piece of evidence. Agents debate the significance of that evidence—for example, one might argue an alert is false positive while another pushes for immediate containment.

You feed agents endpoint activity logs (`list_endpoint_activity_logs`), recent detections, and detailed security alerts. The data guides the conversation toward a consensus decision.

Yes. Because agents debate, they naturally create multi-step pipelines where the output of one tool call becomes the critical input for the next agent's argument.

The system focuses on metadata related to security events, such as asset names, detection IDs, and network object details. It keeps the scope technical rather than personal.

Yes. The framework supports connecting tools from different MCP Servers, allowing your agents to aggregate intelligence across various security domains and endpoints.

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