How to Use the AirOps MCP in AutoGen

Q: How do AutoGen agents handle AirOps execution failures?

If getexecutionstatus returns a failed state, the monitoring agent flags the error in the group chat. The other agents then analyze the failure message and decide whether to retry or trigger cancelexecution.

Let your AutoGen agents debate and execute AirOps workflows through this secure, unified MCP Server.

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Connect AirOps MCP to AutoGen

Create your Vinkius account to connect AirOps 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 AirOps with AutoGen

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Execute AirOps workflows via multi-agent consensus

The `execute_workflow_sync` tool triggers immediate workflow runs once your agents reach a consensus. In an AutoGen setup, a performance agent might suggest running a task, while a compliance agent reviews the parameters first. Only after both agents agree does the system fire the tool to execute the run. This collaborative approach prevents accidental or unauthorized API execution. You define the negotiation rules, and your agents handle the validation. The result is a highly secure, self-regulating workflow execution system.

Coordinate background tasks using this MCP Server

The `execute_workflow_async` tool starts background processes, allowing your agent group to continue debating other tasks while the job runs. One agent monitors progress using `get_execution_status`, while another prepares the next steps based on the expected output. If the monitoring agent detects an error, it calls `cancel_execution` to halt the run immediately. This parallel execution model makes your multi-agent conversations highly efficient. Agents do not sit idle waiting for network responses. They coordinate tasks, manage execution states, and handle failures through natural conversation.

Share context through a shared memory store

The `search_memory_store` tool retrieves historical context that your agents use to settle debates. When agents disagree on a fact, they query the shared vector database to find historical records. Once they resolve the issue, they write the new consensus back to the store using `add_memory_document`. This shared memory layer ensures all agents stay aligned over long conversations. Instead of passing massive message histories back and forth, agents query the vector database dynamically. This MCP integration makes long-term memory management simple.

Setup guide

Set up AirOps 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 AirOps 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="AirOps_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about AirOps MCP in AutoGen

Install the AutoGen MCP extension and use the server tools helper to retrieve the tool list. Then, pass this list directly into your AssistantAgent constructor. The adapter handles all schema conversions automatically.

Yes, you can configure a group chat where agents discuss the inputs before executing a tool. For example, one agent drafts the parameters for `execute_workflow_sync` while another validates them. The tool is only called once they agree.

If `get_execution_status` returns a failed state, the monitoring agent flags the error in the group chat. The other agents then analyze the failure message and decide whether to retry or trigger `cancel_execution`.

Yes, you can connect using either transport layer depending on your architecture. For hosted environments on Vinkius, the Streamable HTTP transport provides the most reliable connection.

Absolutely, all communication between AutoGen and the server is encrypted using TLS 1.3. Vinkius executes every tool inside a zero-trust V8 isolate sandbox, ensuring your workflow parameters and conversation logs are completely isolated and wiped instantly after execution.

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