How to Use the Worksection MCP in AutoGen

Q: Can I simulate a debate around project status using Worksection and AutoGen?

Yes. You can set up agents that argue over whether completetask or stoptimer is needed, forcing them to use the MCP Server's tools in conversation until they converge on a definitive decision.

Q: What if I need to track multiple resources with Worksection and AutoGen?

The agents can assign roles—one tracks timers using listactivetimers, while another manages projects via listprojects. They debate which resource needs attention first, leading to a prioritized plan.

Worksection: Consensus-driven decision making with AutoGen.

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Connect Worksection MCP to AutoGen

Create your Vinkius account to connect Worksection 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 Worksection with AutoGen

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Debating Project Scope via MCP Server

AutoGen allows agents to debate project decisions. One agent might call `list_projects` and declare the scope is too vague. Another agent, using `get_project_details`, counters with specific resource requirements until a consensus is reached.

Automated Task Resolution with AutoGen

The system can resolve complex task issues through debate. One agent identifies an incomplete task using `list_project_tasks`; another suggests the fix by calling `reopen_task`. The agents negotiate until a final, agreed-upon action is taken.

Analyzing Team Composition with AutoGen

Need to know who should work on something? A team of agents debates membership. One agent calls `list_project_members`, while another cross-references this with the full user pool from `list_all_users` to recommend the best person.

Setup guide

Set up Worksection 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 Worksection 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="Worksection_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

AutoGen doesn't just execute; it makes multiple agents talk. When dealing with Worksection tools, they discuss which tool to call and in what order until all competing perspectives agree on the final action.

Yes. You can set up agents that argue over whether `complete_task` or `stop_timer` is needed, forcing them to use the MCP Server's tools in conversation until they converge on a definitive decision.

The agents can assign roles—one tracks timers using `list_active_timers`, while another manages projects via `list_projects`. They debate which resource needs attention first, leading to a prioritized plan.

If an agent determines that a task was improperly closed, it can propose calling `reopen_task`. Other agents might debate if additional data from `get_task_details` is needed before accepting the proposal.

This server touches project info, task records, and user profiles. The tools handle interactions with `list_project_members`, `get_project_details`, and the core user/task data.

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