How to Use the Aero Workflow MCP in AutoGen

Let your AutoGen agents debate and execute Aero Workflow tasks for consensus-driven accounting management.

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Connect Aero Workflow MCP to AutoGen

Create your Vinkius account to connect Aero Workflow 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 Aero Workflow with AutoGen

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Debate tasks using Aero Workflow

Assign one agent to audit `list_workflow_emails` while another proposes responses. They negotiate the best approach based on the specific firm requirements. Conflict resolution happens in the conversation. You get a final decision that considers both speed and accuracy requirements.

Automate Aero Workflow with multi-agent consensus

Create a team where a security agent monitors `create_new_customer` calls. It flags suspicious entries while the task agent pushes for completion. This structure prevents errors in your CRM. The agents check each other's work before committing changes to your account database.

Convert Aero Workflow schema for AutoGen

The McpToolAdapter maps tools like `list_configured_webhooks` to agent functions automatically. You skip the manual boilerplate and focus on agent instructions. The integration handles the transport layer. Your agents communicate with the server over HTTP or stdio without extra configuration.

Setup guide

Set up Aero Workflow 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 Aero Workflow 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="Aero Workflow_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install the extension, define your server parameters, and pass the tools to your AssistantAgent. The adapter handles all schema conversions automatically.

Yes, agents can debate the results of any tool call. If one agent finds a discrepancy in `list_time_tracking_logs`, another can suggest a correction.

The conversation history serves as the state. Agents reference previous tool outputs to make informed decisions throughout the entire discussion.

You define a shared tool list for the group chat. Every agent in the conversation can invoke the tools to contribute to the final goal.

The server uses a zero-trust model where data stays within your local environment. Only the authorized agents process your firm's sensitive task details.

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