How to Use the UnifyApps MCP in AutoGen

Q: How do I track flow history using UnifyApps and AutoGen?

The agents use listflowexecutions to gather the recent run data. They then debate what these results mean, challenging assumptions about success or failure before concluding.

Q: What details about my integrations can UnifyApps and AutoGen provide?

The agents use getintegrationdetails to get specific information on a single integration. They then debate if the provided details meet the criteria needed for their final decision.

Q: Can I list all configured AI agents with UnifyApps and AutoGen?

Yes, listaiagents provides the initial inventory of agents. The agents then use this list to decide which agent should take the lead on a given task.

Q: Does UnifyApps and AutoGen help audit connections?

The process starts with listactiveconnections. Multiple agents discuss the status of these accounts, ensuring all necessary connection points are available before proceeding.

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Coordinate Automation Flow Checks

A multi-agent system can use `list_automation_flows` to gather all defined workflows. The agents then pass this list to a second agent that calls `list_flow_executions`. This setup allows the team of agents to debate whether certain flows should be run, analyzing past performance metrics before converging on a decision.

Verify System Integrations

If an agent needs to act on connections, it first calls `list_active_connections` to see what's available. Then, another agent uses `get_integration_details` to verify the specific scope and parameters of a required integration. The agents challenge each other: 'Is this connection active enough?' vs. 'We need details on that specific endpoint.'

Map Out Platform Assets

The initial agent can call `list_integrations` to map all configured assets in UnifyApps. A second agent then calls `list_ai_agents`, comparing the asset list against known internal resources. The agents discuss discrepancies: 'Wait, why isn't this integration showing up?' This process ensures thorough system auditing.

Setup guide

Set up UnifyApps 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 UnifyApps 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="UnifyApps_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about UnifyApps MCP in AutoGen

The agents use `list_flow_executions` to gather the recent run data. They then debate what these results mean, challenging assumptions about success or failure before concluding.

The agents use `get_integration_details` to get specific information on a single integration. They then debate if the provided details meet the criteria needed for their final decision.

Yes, `list_ai_agents` provides the initial inventory of agents. The agents then use this list to decide which agent should take the lead on a given task.

The process starts with `list_active_connections`. Multiple agents discuss the status of these accounts, ensuring all necessary connection points are available before proceeding.

This server deals with system configuration metadata. You can check active account connections using `list_active_connections` to confirm the state of linked accounts.

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