How to Use the IBM QRadar MCP in AutoGen

Q: How does the AutoGen MCP adapter handle QRadar tool schemas?

The McpToolAdapter automatically translates the QRadar JSON schemas into the format AutoGen expects. This ensures that your agents call tools like updateoffense or getlogsources with perfectly formatted arguments every time.

Q: How does AutoGen protect our QRadar correlation rules?

When your agents check correlation rules via getrules, the payload is processed in-memory and immediately destroyed after the agent session closes. Vinkius uses ephemeral, isolated MCP instances, ensuring your defense logic remains completely secure.

Deploy debating AutoGen agents to analyze IBM QRadar offenses, run Ariel queries, and coordinate incident response.

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Connect IBM QRadar MCP to AutoGen

Create your Vinkius account to connect IBM QRadar to AutoGen 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 AutoGen

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Multi-agent debate for QRadar offense triage

AutoGen lets you set up specialized agents that collaborate on IBM QRadar offenses using the IBM QRadar MCP Server. A triage agent uses `get_offenses` to spot new threats, while a separate analyst agent calls `get_offense_details` to dissect the payload. They debate the severity before taking action. If they disagree, a third audit agent pulls the relevant correlation rules using `get_rules` to settle the dispute. This consensus-driven approach prevents false positives from triggering automated blocks.

Collaborative threat hunting via Ariel queries on MCP Server

Running deep log searches requires coordination via this MCP Server. One AutoGen agent formulates and runs an Ariel query using `execute_aql`, while a second agent monitors the progress with `get_aql_status` and gets the logs via `get_aql_results`. Once the logs are back, a third agent checks `get_log_sources` to check that the reporting endpoints are configured correctly. By dividing these tasks, your AutoGen team executes complex threat hunts without blocking your main console.

Coordinate automated offense updates with human-in-the-loop

Never let an agent blindly close a critical alert. With AutoGen using this MCP integration, a security agent drafts an update using `update_offense` and presents it to a human supervisor agent for final approval. Before proposing the update, the agent gathers evidence by pulling network zones with `get_network_hierarchy` and matching them against active reference sets via `get_reference_sets`. You get fully documented, safe remediation steps.

Setup guide

Set up IBM QRadar 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 IBM QRadar 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="IBM QRadar_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent IBM QRadar 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 IBM QRadar. 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 IBM QRadar MCP in AutoGen

Multiple agents use different tools to triage the same alert. For example, one agent calls `get_offense_details` to gather context, while another queries `get_rules` to analyze the trigger, allowing them to debate the threat level before taking action.

Yes, your agent group runs multiple searches concurrently. Each agent initiates its search via `execute_aql` and polls `get_aql_status` independently, preventing a single slow query from stalling your entire multi-agent workflow.

The `McpToolAdapter` automatically translates the QRadar JSON schemas into the format AutoGen expects. This ensures that your agents call tools like `update_offense` or `get_log_sources` with perfectly formatted arguments every time.

While the current toolset allows you to view watchlists using `get_reference_sets`, any modifications to those sets must be handled through other integration points or manual console actions.

When your agents check correlation rules via `get_rules`, the payload is processed in-memory and immediately destroyed after the agent session closes. Vinkius uses ephemeral, isolated MCP instances, ensuring your defense logic remains completely secure.

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