How to Use the Toggl Plan MCP in Google ADK

Q: How can I process Toggl Plan timeline data using the Google ADK?

You first use tools like listtimelinetasks to pull task details. Because of native BigQuery integration, your agent can take that raw output and run complex queries against it without leaving the Google Cloud ecosystem.

Q: What kind of data does Toggl Plan store that I can access with the Google ADK?

You can retrieve user lists via listworkspaceusers, project metadata using getprojectdetails, and categorization tags by calling listworkspacetags. This covers most team resource information.

Q: Does the Google ADK support creating new tasks on Toggl Plan?

Yes. Your agent uses createtimelinetask to add new items directly to the timeline, requiring only a workspace ID, project ID, and task name.

Q: Is using Google ADK for Toggl Plan reliable for updates?

It's built for enterprise use. The updatetimelinetask tool accepts a JSON object to modify existing tasks. Since it integrates with Gemini, the agent can reason about why that update is needed.

Build enterprise agents for Toggl Plan using the Google ADK framework.

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Visualize and Manipulate Workflows

The `list_timeline_tasks` tool lets your agent query every task on the Toggl Plan timeline for a specific workspace ID. This provides the necessary raw data set, which can then be fed into BigQuery for deep analysis. After reading the state of the tasks, you can modify them using `create_timeline_task`. Your agent doesn't just read; it acts on the project schedule to keep things moving.

Manage Project Scope and Users

When building an enterprise system, knowing who does what matters. Use `list_workspace_projects` to enumerate all projects in a workspace. You can then cross-reference this with user data from `list_workspace_users`. This helps your agent map responsibilities across large teams. The tool `get_project_details` pulls specific project metadata, which is vital context when feeding the plan into long-context Gemini models.

Maintain Data State and Context

If a task needs an adjustment—maybe a deadline shifts or scope changes—the agent handles it using `update_timeline_task`. It takes a structured JSON object, ensuring all necessary fields are present for a successful update. For cleanup, the `delete_timeline_task` tool permanently removes tasks. Use this in conjunction with `get_task_details` to confirm you're deleting exactly what was intended.

Setup guide

Set up Toggl Plan MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Toggl Plan tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Toggl Plan_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Toggl Plan tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Toggl Plan MCP in Google ADK

You first use tools like `list_timeline_tasks` to pull task details. Because of native BigQuery integration, your agent can take that raw output and run complex queries against it without leaving the Google Cloud ecosystem.

You can retrieve user lists via `list_workspace_users`, project metadata using `get_project_details`, and categorization tags by calling `list_workspace_tags`. This covers most team resource information.

Yes. Your agent uses `create_timeline_task` to add new items directly to the timeline, requiring only a workspace ID, project ID, and task name.

It's built for enterprise use. The `update_timeline_task` tool accepts a JSON object to modify existing tasks. Since it integrates with Gemini, the agent can reason about *why* that update is needed.

The server handles productivity records including task names, project IDs, workspace IDs, and user association data. This allows your agent to manage core team scheduling information.

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