How to Use the Structured MCP in AutoGen

Q: How do I manage tasks using Structured and AutoGen?

The agents can debate whether to use createtask or updatetask. They compare the current state (read via gettaskdetails) against the desired goal, leading to a consensus decision.

Q: What is the role of Structured and AutoGen in data governance?

The agents can run checks using listtasks and then use that list to verify if all necessary tasks have been accounted for. This structured checking process prevents gaps.

Q: Does this MCP Server support reviewing multiple plans with Structured and AutoGen?

Yes. Agents can first run listplans, gathering all options, and then pass them sequentially to a verification agent that runs getplandetails on each one.

Get consensus on tasks: AutoGen's Structured MCP Server for deliberation.

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Debate task creation with the MCP Server.

When your agents need to decide if a task should exist, they can use `create_task`. One agent might argue the task is low priority, while another uses `get_plan_details` to prove it belongs in the current plan. The debate converges on the best outcome.

Reviewing plans requires AutoGen and MCP Server.

If agents are unsure which plan is correct, they can call `list_plans`. They then pass this list to a second agent, who uses `get_plan_details` to verify the scope. The resulting discussion determines the next authoritative step.

Managing user context with AutoGen.

AutoGen agents can challenge each other on permissions or roles by comparing data from `get_user_profile`. This ensures that any action taken, like calling `update_task`, is validated against the current user's scope.

Setup guide

Set up Structured 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 Structured 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="Structured_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

The agents can debate whether to use `create_task` or `update_task`. They compare the current state (read via `get_task_details`) against the desired goal, leading to a consensus decision.

The agents can run checks using `list_tasks` and then use that list to verify if all necessary tasks have been accounted for. This structured checking process prevents gaps.

Yes. Agents can first run `list_plans`, gathering all options, and then pass them sequentially to a verification agent that runs `get_plan_details` on each one.

The MCP Server primarily handles structured task and plan metadata. The user profile accessed via `get_user_profile` is the key piece of personal information managed here.

You feed tool outputs into your multi-agent conversation. For instance, one agent calls `list_plans`, and the output becomes the context for two other agents to debate the next steps.

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