How to Use the Airbrake MCP in AutoGen

Q: How can my AutoGen agents decide if an Airbrake error is critical?

You assign different roles. One agent calls listerrorgroups to get the raw data, and another agent analyzes the notice count and error message. They debate the priority in their group chat before deciding.

Q: Can one AutoGen agent track deploys in Airbrake while another watches for errors?

Yes, that's a perfect use case. Give the trackdeploy and listdeploys tools to a 'DeployManager' agent, and the listerrorgroups tool to a 'Monitor' agent. They can then inform each other of their findings.

Q: Can AutoGen agents investigate an Airbrake error and check recent deploys?

Absolutely. An 'Error' agent can use geterrorgroup to get details, while a 'DevOps' agent uses listdeploys to get context. They share their results in the agent conversation to find a correlation.

Q: How do the agents get a list of all projects to monitor?

You can have a 'Planner' agent call listprojects at the start. It can then assign different projects to different 'Worker' agents to monitor in parallel.

Assemble a team of AutoGen agents to debate, diagnose, and act on Airbrake errors.

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Setup Airbrake with AutoGen

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Let Agents Debate Error Priority

AutoGen lets you build a team. Create a 'Sentry' agent that only watches for new issues using `list_error_groups`. When it spots one, it notifies an 'Analyst' agent in the group chat. The Analyst's job is to dig deeper with `get_error_group` and `list_notices`. The two agents can then discuss the error's severity and potential impact, reaching a consensus before they ever bother a human.

Decide on Rollbacks Through Conversation

This is where multi-agent systems really pay off. A 'Deploy' agent calls `track_deploy` to announce a new release. A 'Sentry' agent sees a related spike in errors and raises an alarm. They bring in a 'QA' agent, who uses the error details to form an opinion. The agents can then debate if the deploy is the cause and vote on recommending a rollback. This MCP Server provides the facts they need to have a meaningful argument.

Build an Autonomous DevOps Team for Airbrake

You can assign very specific roles. One agent's only job is to check API health with `check_airbrake_status`. Another is the 'Project Manager', using `list_projects` to keep an inventory. A third is the 'Technician', focused on errors. They don't operate in a vacuum; they talk to each other. When the Technician finds an error in a project it doesn't recognize, it asks the Project Manager for context. It’s a conversational workflow that mirrors how a real team solves problems.

Setup guide

Set up Airbrake 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 Airbrake 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="Airbrake_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You assign different roles. One agent calls `list_error_groups` to get the raw data, and another agent analyzes the notice count and error message. They debate the priority in their group chat before deciding.

Yes, that's a perfect use case. Give the `track_deploy` and `list_deploys` tools to a 'DeployManager' agent, and the `list_error_groups` tool to a 'Monitor' agent. They can then inform each other of their findings.

Absolutely. An 'Error' agent can use `get_error_group` to get details, while a 'DevOps' agent uses `list_deploys` to get context. They share their results in the agent conversation to find a correlation.

You can have a 'Planner' agent call `list_projects` at the start. It can then assign different projects to different 'Worker' agents to monitor in parallel.

Agents will pass around data from tool calls, like error notice text and project configuration details. Vinkius secures the initial MCP tool calls, and the data is then handled within your AutoGen agent framework.

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