How to Use the Senar.io MCP in OpenAI Agents SDK

Secure your infrastructure by letting your OpenAI Agents SDK automatically control and monitor Senar.io security training.

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MCP Servers — Included with Plan

Connect Senar.io MCP to OpenAI Agents SDK

Create your Vinkius account to connect Senar.io to OpenAI Agents SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Senar.io with OpenAI Agents SDK

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Assign training targets using OpenAI Agents SDK

Your agent handles the busywork of setting up new hires. It calls `create_user_and_assign` to spin up a profile and drop them straight into a specific active response simulator collection without manual intervention. If you need to audit who is currently in the system, the agent runs `list_users` to pull the active roster. This keeps your training pipelines moving without requiring a human operator to click through a web dashboard.

Track simulator progress with this MCP Server

Get raw telemetry from active training runs on demand. Your agent invokes `get_activity_data` and `get_user_sessions` to watch user behavior in real time, catching security anomalies before they turn into actual system-wide incidents. The agent aggregates these metrics by querying `get_progress` for specific team members. You get an instant snapshot of who finished their security drills and who is lagging behind.

Push threat intel directly into training collections

Feed fresh threat intelligence directly into your simulation environments. When your agent identifies a new attack pattern, it uses `add_content` through the MCP toolset to inject the scenario details. The agent verifies the injection by calling `get_collection_details` and `list_collections`. This ensures your team trains against the exact tactics hackers are using right now.

Setup guide

Set up Senar.io MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Senar.io tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Senar.io tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Senar.io tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Senar.io Agent",
            instructions="You have access to Senar.io tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Senar.io MCP in OpenAI Agents SDK

Install the package using pip install openai-agents. Configure the connection using MCPServerStreamableHttp with your endpoint token, then pass it directly into your Agent constructor.

It's easy to restrict access using the built-in guardrails in the SDK to inspect and validate tool calls before they run. You can intercept calls to sensitive tools like `create_user_and_assign` before execution.

Yes, you should set cacheToolsList=True in your streamable HTTP configuration. That's how you keep performance high and prevent unnecessary roundtrips during active training runs.

You can set up specialized agents for different tasks. One agent can call `get_user_sessions` to flag inactive users, then hand off the session token to a communications agent to ping the user.

The server processes your active session logs and user details inside an isolated V8 sandbox. Our zero-trust MCP architecture ensures that no raw user training data is stored permanently on our servers, keeping your internal metrics secure.

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