How to Use the Everbridge Critical Management MCP in AutoGen

Build debate-driven multi-agent systems that negotiate incident resolution using Everbridge Critical Management tools.

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Connect Everbridge Critical Management MCP to AutoGen

Create your Vinkius account to connect Everbridge Critical Management 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 Everbridge Critical Management with AutoGen

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Debate Incident Severity and Response

Assign agents to analyze incidents using `list_high_severity_incidents` while another agent validates the findings. They debate the urgency of each event before deciding on an action. This approach prevents knee-jerk reactions during outages. The agents challenge each other's conclusions, ensuring every notification follows your predefined safety protocols.

Consensus-Driven Alert Dispatch

Your agents use `list_critical_contacts` and `list_contact_distribution_groups` to agree on the correct notification path. They negotiate the best communication channel based on the current incident type. This creates a system where dispatch decisions are verified by multiple specialized agents. You get a documented conversation showing exactly why a specific group was notified.

Automated Audit and Review Cycles

Deploy an auditor agent that runs `quick_crisis_event_audit` after every incident to verify resolution accuracy. It flags discrepancies between the response plan and the actual outcome. This closes the loop on your incident management process. Your agents learn from past audit data, improving their performance in future critical events.

Setup guide

Set up Everbridge Critical Management 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 Everbridge Critical Management 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="Everbridge Critical Management_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Everbridge Critical Management data")
print(result.messages[-1].content)

Get your connection token →

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="Everbridge Critical Management_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Everbridge Critical Management data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Everbridge Critical Management. 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 Everbridge Critical Management MCP in AutoGen

Yes. You register the tools to your agents, and they include tool output in their conversation. This allows them to compare data from different sources before agreeing on a next step.

Use the McpToolAdapter to register the server tools. Once registered, your agents call these tools as part of their standard debate loop.

It provides the raw data that agents use to form their arguments. By querying `get_incident_detailed_data`, agents base their debate on facts rather than assumptions.

You pass the API key and organization ID as environment variables to the server. The agents interact with the API through the established secure connection without handling the keys themselves.

All contact methods and profile details are kept within the private agent conversation memory. No data is shared externally, and access is restricted to the agents within your runtime environment.

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