How to Use the Elastic Security MCP in AutoGen

Q: Can AutoGen agents coordinate to disable noisy rules in Elastic Security?

Yes, a monitoring agent can identify high-noise rules via searchsignals and debate with a policy agent. Once they agree, the execution agent calls updaterule to disable the rule.

Q: How do AutoGen agents verify rule updates in Elastic Security?

One agent proposes rule changes using createrule, while a peer agent validates the setup by calling getrule. This ensures all MITRE TTP mappings and risk scores are correct before deployment.

Let security and compliance agents debate Elastic Security alerts and tune rules in AutoGen.

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Create your Vinkius account to connect Elastic Security 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 Elastic Security with AutoGen

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Debate threat alerts with multi-agent workflows

`search_signals` feeds raw security alerts into an AutoGen conversation where specialized agents analyze the threat. A security agent might argue for immediate containment while an operations agent checks for business impact. The agents negotiate the risk score and process tree before deciding on the next step. This consensus-driven approach prevents knee-jerk reactions and ensures balanced incident response.

Collaborative rule tuning in AutoGen

`update_rule` is triggered only after your AutoGen agents reach a consensus on rule performance. If an alert generator triggers too many false positives, the tuning agent proposes a modification. A separate validation agent runs `get_rule` to review the query logic before allowing the update. This multi-agent verification loop guarantees that rule changes do not introduce critical coverage gaps.

Automated exception negotiation

`add_exception` is invoked after a structured debate between your AutoGen agents regarding a noisy hostname. The analyst agent presents the exception request, and the compliance agent audits it. The agents check existing bypasses via `list_exceptions` to ensure the new rule does not violate corporate security policies. Once approved, the MCP Server applies the whitelist directly to the SIEM.

Setup guide

Set up Elastic Security 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 Elastic Security 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="Elastic Security_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Elastic Security 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="Elastic Security_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Common questions about Elastic Security MCP in AutoGen

Initialize the MCP Server tools using the autogen-ext package and pass them to your AssistantAgent constructor. This lets your agents call `search_signals` and `get_rule` during their conversational loops.

Yes, a monitoring agent can identify high-noise rules via `search_signals` and debate with a policy agent. Once they agree, the execution agent calls `update_rule` to disable the rule.

One agent proposes rule changes using `create_rule`, while a peer agent validates the setup by calling `get_rule`. This ensures all MITRE TTP mappings and risk scores are correct before deployment.

The conversation pauses or escalates to a human administrator. The agents use `list_exceptions` to present their arguments with data, but no changes are made until they reach consensus.

Yes, all security alerts, rule configurations, and exception lists are processed in an isolated MCP Server sandbox. Vinkius secures your credentials, ensuring that agent conversations do not leak sensitive SIEM data to unauthorized channels.

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