How to Use the Toggl Plan MCP in AutoGen

Q: How do I manage task deletion with Toggl Plan and AutoGen?

If the team decides a segment is obsolete, an agent runs deletetimelinetask. Because of the high stakes, this process requires consensus before executing the irreversible action.

Q: Can I check project scope with Toggl Plan and AutoGen?

Yes. Agents debate using data from listworkspaceprojects to validate the required scope against defined milestones (listmilestones). This consensus builds a verified plan.

Q: Is Milestone tracking supported with Toggl Plan and AutoGen?

Absolutely. The agents can use listmilestones as checkpoints during their deliberation, ensuring that any proposed schedule aligns with the critical path markers you define.

Q: What happens if I need to update a task with Toggl Plan and AutoGen?

An agent can propose an update using updatetimelinetask. The system then runs through a debate, allowing other agents to challenge the proposed JSON object until consensus is reached on the correct modification.

Get consensus on complex scheduling decisions using AutoGen.

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Validate and correct schedules

You can set up a debate where one agent checks the current plan using `get_task_details`. A second agent might identify discrepancies, prompting a third to run `update_timeline_task` to resolve it. This multi-agent approach ensures that critical changes are validated against existing data before commitment.

Assess project readiness

To determine if a project is ready, agents debate using inputs from `list_workspace_projects` and `list_milestones`. One agent lists projects; another checks for milestones. They negotiate which phases are complete. The consensus decision then dictates the next action required on the timeline.

Map out full work scope

An autonomous system can map the entire working scope by first calling `get_timeline_view` to set boundaries. Then, it lists all available team members via `list_workspace_members`. The agents use this combined dataset to structure a comprehensive project plan.

Setup guide

Set up Toggl Plan 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 Toggl Plan 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="Toggl Plan_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

If the team decides a segment is obsolete, an agent runs `delete_timeline_task`. Because of the high stakes, this process requires consensus before executing the irreversible action.

Yes. Agents debate using data from `list_workspace_projects` to validate the required scope against defined milestones (`list_milestones`). This consensus builds a verified plan.

Absolutely. The agents can use `list_milestones` as checkpoints during their deliberation, ensuring that any proposed schedule aligns with the critical path markers you define.

An agent can propose an update using `update_timeline_task`. The system then runs through a debate, allowing other agents to challenge the proposed JSON object until consensus is reached on the correct modification.

The server handles timeline task data, including names, start dates, end dates, and associated project information. These structured scheduling details are subject to multi-agent validation protocols.

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