How to Use the Trend Micro MCP in Pydantic AI

Q: Can I use Pydantic AI for Trend Micro account checks?

Yes. The agent uses the toolset to call getvisiononeaccount, and Pydantic validates that the returned connectivity status field is a boolean, ensuring your subsequent logic works.

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Connect Trend Micro MCP to Pydantic AI

Create your Vinkius account to connect Trend Micro to Pydantic AI 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 Trend Micro with Pydantic AI

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Get Detailed Security Alerts

This is where the correctness matters. When you call `list_security_alerts` or use `get_alert_details`, Pydantic ensures every field—like severity or affected asset—is exactly what your code expects. No silent failures, no unexpected fields getting passed to downstream systems. You know the data is correct before it runs.

Check Endpoints and Assets

Need to verify a device? The agent can list managed assets using `list_managed_endpoints`. If an API call returns messy data, Pydantic catches it. This keeps your agent's logic sound when querying logs from `list_endpoint_activity_logs`. It makes your security automation predictable.

Analyze Detected Threats

When running a threat hunt, you need reliable data. The framework validates results from `list_recent_detections`, allowing the agent to trust that fields like detection name and time are accurate. This type-safe approach is critical when using security intelligence tools like those provided by `list_suspicious_objects`.

Setup guide

Set up Trend Micro MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "trend-micro-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Trend Micro tools.",
)

result = await agent.run("List recent Trend Micro transactions")
print(result.output)

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Common questions about Trend Micro MCP in Pydantic AI

The MCP Server provides the raw telemetry, but Pydantic validates it. This means if `list_security_alerts` returns an unexpected format, your agent fails loudly and tells you exactly what's wrong.

Yes. The agent uses the toolset to call `get_vision_one_account`, and Pydantic validates that the returned connectivity status field is a boolean, ensuring your subsequent logic works.

It exposes structured security information including alert details (severity, description), endpoint logs, and suspicious objects (URLs, IPs, files).

Yeah. A basic wrapper just calls the function; Pydantic validates the *output*. It's the difference between hoping the data is clean and knowing, with certainty, that it is.

You get endpoint activity telemetry and email activity logs. Crucially, Pydantic validates the structure of these complex log records before your agent processes them.

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