How to Use the IBM QRadar MCP in OpenAI Agents SDK

Q: Can OpenAI Agents SDK execute AQL searches in IBM QRadar?

Yes. You trigger executeaql through your agent, and the server handles the asynchronous search process. Your agent then fetches the results once the status checks complete.

Q: Does IBM QRadar integration with OpenAI Agents SDK support real-time updates?

The server provides updateoffense to let your agent modify incident status or add notes. You define the logic in your agent to decide when an offense requires a manual or automated update.

Connect IBM QRadar to your OpenAI Agents SDK workflow for instant, validated incident response and security data analysis.

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Connect IBM QRadar MCP to OpenAI Agents SDK

Create your Vinkius account to connect IBM QRadar to OpenAI Agents SDK 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 IBM QRadar with OpenAI Agents SDK

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Automate QRadar offense triage

Your agent pulls active incidents directly from your console using `get_offenses`. It avoids manual navigation by grabbing the specific context needed for each case. Once the agent identifies a priority alert, it uses `get_offense_details` to build a complete picture. You get a clear summary of the threat without opening the dashboard.

Run complex AQL searches

Fire off `execute_aql` commands to hunt for specific indicators across your environment. The agent manages the lifecycle of your queries automatically. It tracks progress with `get_aql_status` and pulls the final data using `get_aql_results`. Your agent waits for the data to be ready, then processes it against your security policies.

Manage security metadata

Your agent maps threats to your infrastructure by querying `get_network_hierarchy` and `get_log_sources`. It understands exactly where an event originated. It also checks `get_reference_sets` and `get_rules` to compare current activity against known patterns. This context ensures the agent makes decisions based on your actual network configuration.

Setup guide

Set up IBM QRadar 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 IBM QRadar tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate IBM QRadar 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="IBM QRadar Agent",
            instructions="You have access to IBM QRadar 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 IBM QRadar MCP in OpenAI Agents SDK

You instantiate the MCP server via your agent configuration and call the exposed tools directly. The agent automatically discovers `get_offenses` and triggers the request when you ask for current incidents.

Yes. You trigger `execute_aql` through your agent, and the server handles the asynchronous search process. Your agent then fetches the results once the status checks complete.

The server provides `update_offense` to let your agent modify incident status or add notes. You define the logic in your agent to decide when an offense requires a manual or automated update.

We use a secure, ephemeral connection model. You provide your endpoint token to the Vinkius host, and the server manages the handshake without exposing your secrets to the agent environment.

Your logs and offense data never leave your controlled infrastructure. The MCP server acts as a local bridge, ensuring raw data remains within your perimeter while only relevant summaries return to the agent.

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