How to Use the Worktile MCP in AutoGen

Make agents debate Worktile actions and converge on a consensus decision using AutoGen.

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Connect Worktile MCP to AutoGen

Create your Vinkius account to connect Worktile 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 Worktile with AutoGen

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Perplexity

Consensus on Task Prioritization

A multi-agent setup can simulate prioritization. One agent might call `get_project` to understand the scope, while another calls `list_tasks`. They then debate which tasks are most critical and propose an update via `update_task`, converging on a single action.

Debating Team Communication Needs

You can set up agents to discuss team structure. One agent uses `get_team_info` while another checks communication channels with `list_channels`. They debate whether the current channel list is sufficient before recommending a new message via `send_message`.

Auditing Worktile Project Scope

For auditing, you can deploy agents to check project boundaries. One agent runs `list_projects`, and another uses `get_project` on the results. They challenge each other's assumptions about scope until they agree on a final list of work items.

Setup guide

Set up Worktile 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 Worktile 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="Worktile_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

AutoGen allows you to build systems where multiple agents discuss and challenge each other's conclusions. They don't just run a script; they negotiate actions, like deciding which member needs an update via `update_task`.

Yes. Because the framework is conversation-driven, it handles complexity well. You can simulate a full product management cycle where agents work together to plan tasks using tools like `create_task` and `list_tasks`.

The system forces deliberation. If one agent suggests adding a resource (`create_task`), another agent can challenge that suggestion by checking existing team capacity via `get_team_info`, leading to a refined, agreed-upon action.

It touches task and project metadata. Agents can read basic information using tools like `list_projects` or `get_project`, but the strength is forcing them to discuss *how* that data should be used.

The agents are limited only by the tools provided. If you give them `list_members`, they'll use it, but if you don't include a tool to send messages, they won't magically know how to call `send_message`.

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