How to Use the Workload MCP in AutoGen

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Connect Workload MCP to AutoGen

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Setup Workload with AutoGen

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Negotiating Workload Decisions with AutoGen

When building a multi-agent system, sometimes you need to decide if an automation should run. Agents can debate this using the `check_workload_status` and `get_workflow` tools. A security agent might challenge running a workflow unless it's disabled first via `disable_workflow`, forcing consensus on safe execution.

Managing Workload State in AutoGen

The agents can collectively manage the lifecycle of an automation. They use `list_workflows` to see what exists, and then they decide whether a new workflow needs to be created via `create_workflow`. This ensures that all competing perspectives converge on a single, agreed-upon operational state.

Auditing Workload Failures with AutoGen

If an execution fails, the agents don't just quit. They use `list_logs` to review the failure details and then decide if a simple `retry_execution` is enough. This simulation of human deliberation makes your system robust; it doesn't assume success.

Setup guide

Set up Workload 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 Workload 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="Workload_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about Workload MCP in AutoGen

The tools provide concrete facts for the agents to discuss. For instance, one agent might call `list_connections` while another critiques the findings against a defined workflow.

It moves your system beyond simple sequential calls. The agents debate actions like enabling or disabling workflows (`enable_workflow`/`disable_workflow`) until they reach a consensus decision.

The server exposes 13 tools, allowing your AssistantAgent to select and call any of them. The agents then discuss whether that tool call is appropriate given their goals.

Yes. By giving the tools list, you empower the multi-agent system to build and manage entire automation blueprints using `create_workflow`.

It manages connection metadata, workflow definitions, execution status, and detailed system logs that the agents use for debate.

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