How to Use the LiftedWork MCP in Google ADK

Q: Can I restrict which LiftedWork tools my Google ADK agent can access?

Yes. You can use the optional toolnames filter in the ADK toolset configuration to expose only specific tools like listtasks while blocking write operations.

Q: Does LiftedWork support long-context reasoning in the Google ADK?

Absolutely. Because tools like listtimeentries return structured data, you can dump large volumes of timesheet history directly into Gemini's context window for deep trend analysis.

Connect your Google ADK enterprise agents to active job markets and track workloads via this MCP Server.

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Connect LiftedWork MCP to Google ADK

Create your Vinkius account to connect LiftedWork 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 LiftedWork with Google ADK

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Build Google ADK workflows for agency tasks

The `create_task` tool lets Gemini write new interview milestones directly into your recruitment database. Your agent sends the raw data as a JSON string, and the system handles the write without requiring custom backend code. To organize these tasks, the `create_project` tool sets up dedicated workspaces for new client accounts. This keeps your pipeline clean and ensures every job seeker has a structured space for tracking.

Query active accounts using this MCP Server

The `list_clients` tool dumps your entire client directory straight into Gemini’s context window. Your agent can cross-reference this roster with BigQuery tables to see which accounts need immediate attention. Calling `list_projects` gives the model an instant snapshot of current hiring initiatives. It cuts out manual status updates, letting your cloud agents run on real operational data.

Monitor time tracking and task lists

The `list_tasks` tool gives your agent a live feed of what recruiters are working on right now. It flags bottlenecks and priority items without you writing custom API endpoints. For billing reviews, the `list_time_entries` tool grabs every logged hour on active contract placements. Your agent can match these logs against project budgets to flag cost overruns early.

Setup guide

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

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Common questions about LiftedWork MCP in Google ADK

Initialize the McpToolset with the HTTP transport URL pointing to your managed instance. Pass this toolset directly to your LlmAgent to let Gemini discover the six productivity tools.

Yes. You can use the optional tool_names filter in the ADK toolset configuration to expose only specific tools like `list_tasks` while blocking write operations.

Absolutely. Because tools like `list_time_entries` return structured data, you can dump large volumes of timesheet history directly into Gemini's context window for deep trend analysis.

The connection runs on our hosted infrastructure, which scales automatically. Your agent can query `list_projects` and `list_clients` simultaneously without causing performance degradation.

All timesheet entries, client details, and project structures are processed through our secure MCP gateway via ephemeral, zero-trust sandboxes. No employment data is cached or used for model training, keeping your operational metrics completely private.

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