How to Use the Timeero MCP in Google ADK

Q: Is Timeero tracking data compatible with Google ADK long-context?

Absolutely. You can batch process months of listtimeerotimesheets results into the agent's context window to perform deep historical analysis on labor costs.

Q: Can I filter which Timeero tools Google ADK uses?

Yes. You can pass a toolnames filter to the toolset to restrict the agent's capabilities to only what is necessary for your specific pipeline.

Q: Does Timeero provide GPS coordinates to Google ADK?

Yes. The gettimeerotimesheet tool returns precise GPS telemetry. You can store this directly in your cloud tables for geographic analysis.

Connect Timeero to Google ADK to process field service telemetry and sync labor data directly into your enterprise data pipeline.

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Connect Timeero MCP to Google ADK

Create your Vinkius account to connect Timeero to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Timeero with Google ADK

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Streamline labor tracking with Google ADK

Feed your Gemini-powered agents live timesheet data by integrating `list_timeero_timesheets`. This allows your agents to analyze labor trends or identify discrepancies in field reports immediately. Use `get_timeero_job` to associate specific work hours with project codes. This mapping creates a clean data flow from the field into your existing BigQuery infrastructure.

Sync personnel and schedule data

Your agent uses `list_timeero_users` to keep your internal directory synced with actual field activity. It cuts out the manual sync steps that usually clog up your admin queue. Check `list_timeero_schedules` to see where your team is deployed. The agent pulls these records to ensure your enterprise models stay updated on real-world labor allocation.

Automated status and task management

Ensure your agent is always operational by running `check_timeero_status` as part of your startup sequence. It prevents data ingestion errors by confirming the API is reachable. Use `get_timeero_task` to pull granular details on specific service items. Your agents can then report on task completion rates across your entire service territory using this data.

Setup guide

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

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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 Timeero. 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 Timeero MCP in Google ADK

You instantiate an McpToolset pointing to the Timeero server. Your agent then gains access to all 11 tools, allowing it to query field data as needed.

Absolutely. You can batch process months of `list_timeero_timesheets` results into the agent's context window to perform deep historical analysis on labor costs.

Data is handled via ephemeral connections. The server requires an endpoint token, ensuring only your Google ADK agent can access sensitive field records.

Yes. You can pass a tool_names filter to the toolset to restrict the agent's capabilities to only what is necessary for your specific pipeline.

Yes. The `get_timeero_timesheet` tool returns precise GPS telemetry. You can store this directly in your cloud tables for geographic analysis.

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