How to Use the HCSS MCP in Google ADK

Q: How do I initialize the HCSS MCP Server inside Google ADK?

Use McpToolset with the Vinkius HTTP server parameters. Pass that toolset directly to your LlmAgent to let Gemini call the HCSS tools.

Q: Can I limit which HCSS tools the Google ADK agent can access?

Yes. Use the toolnames filter in the toolset parameters to expose only specific tools like listequipment while hiding financial endpoints.

Connect Gemini's massive context window to your HCSS fleet and project data using the Google ADK.

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Connect HCSS MCP to Google ADK

Create your Vinkius account to connect HCSS 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 HCSS with Google ADK

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Map active construction jobs across your entire enterprise.

Project lists and pre-construction timelines are fed directly into Google ADK agents using our MCP link via the `list_jobs` and `list_precon_projects` tools. This allows Gemini to analyze project timelines alongside your existing big data tables in Google Cloud. You get a clear picture of project status without manual data entry. The agent queries active builds, correlates them with your cloud databases, and flags scheduling conflicts.

Monitor equipment distribution with Google ADK.

Your active fleet telematics are exposed directly to Gemini models through the `list_equipment` and `get_equipment_location` tools. Your agent can process coordinates for hundreds of assets at once using Gemini's long-context capabilities. This helps you spot idle yellow iron instantly. The agent identifies machines that have not moved from their coordinates, letting you reassign them to active projects.

Cross-reference operational business units.

Regional financial structures are mapped by your agent using the `list_business_units` and `list_cost_codes` tools. Your agent queries these endpoints to ensure field expenses match corporate accounting rules. This structure prevents misallocated job costs before they hit BigQuery. You get clean financial tracking without manual reconciliation steps.

Setup guide

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

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Common questions about HCSS MCP in Google ADK

Use `McpToolset` with the Vinkius HTTP server parameters. Pass that toolset directly to your `LlmAgent` to let Gemini call the HCSS tools.

Yes. Use the `tool_names` filter in the toolset parameters to expose only specific tools like `list_equipment` while hiding financial endpoints.

The model uses its long-context window to analyze raw outputs from `list_timecards` and `list_cost_codes`, spotting patterns that normal keyword searches miss.

Yes. The server supports both Stdio and HTTP transports, making it easy to run locally or host in Google Cloud Run.

All requests go through an ephemeral, zero-trust V8 sandbox. Your GPS coordinates and employee IDs are encrypted in transit and never used to train public models.

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