How to Use the DeskTime MCP in Google ADK

Q: Can Google ADK filter which DeskTime tools are available?

Yes, you can restrict access. Use the toolnames parameter in your toolset to only expose endpoints like getcompanyinfo or listprojects.

Q: Can I delete old projects using DeskTime and Google ADK?

Your agent can execute the removeproject tool to clear out archived work. Make sure your prompt includes strict conditions for when deletion is allowed.

Plug DeskTime into Google ADK to analyze massive employee productivity datasets using Gemini's long-context reasoning.

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Connect DeskTime MCP to Google ADK

Create your Vinkius account to connect DeskTime to Google ADK 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 DeskTime with Google ADK

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Analyze Productivity in Google ADK

Gemini models can digest massive amounts of context. When you connect this MCP Server to Google ADK, your agent pulls full organizational data using `get_productivity_reports` and `get_employee_performance`. It processes weeks of time logs in a single prompt. Enterprise teams typically want this data in BigQuery. The agent extracts stats via `list_employees`, formats the records, and pipes them directly into your Google Cloud data warehouse. You get instant visibility into labor costs without writing custom ETL pipelines.

Monitor Active Staff and Projects

Managers need to know who is online right now. Your LlmAgent calls `list_online_staff` to check real-time attendance. If a critical incident occurs, the agent identifies available engineers instantly. Project tracking works the same way. The system runs `list_projects` followed by `get_project_details` to audit active workloads. You can restrict the exposed MCP tools using the tool_names filter if you only want the agent reading data.

Manage DeskTime Tasks via MCP

Stop manually updating task statuses across different platforms. The agent reads your internal Google Chat messages and automatically triggers `create_new_task` or `mark_task_completed` based on developer updates. Creating new initiatives takes seconds. When a Vertex AI pipeline detects a new client contract, the agent fires `create_project` to set up the billing bucket. It then populates the initial requirements using `list_project_tasks`.

Setup guide

Set up DeskTime 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 DeskTime 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="DeskTime_agent",
    model="gemini-2.0-flash",
    instruction="You have access to DeskTime tools via MCP.",
    tools=mcp_tools,
)

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Common questions about DeskTime MCP in Google ADK

Install the package and create a McpToolset using your Vinkius URL. Pass this toolset to your LlmAgent initialization. Both Stdio and HTTP transports work perfectly.

Yes, you can restrict access. Use the tool_names parameter in your toolset to only expose endpoints like `get_company_info` or `list_projects`.

Gemini uses its huge token window to analyze the raw JSON returned by the MCP Server. It reads bulk exports from `get_productivity_reports` and identifies long-term trends across your entire workforce.

Your agent can execute the `remove_project` tool to clear out archived work. Make sure your prompt includes strict conditions for when deletion is allowed.

This server processes individual employee performance stats and active working hours. Vinkius enforces a zero-trust architecture where every request requires a valid endpoint token. The connection remains ephemeral, dropping immediately after the agent receives the time tracking data.

Use it with your favorite AI tools

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