How to Use the Armano MCP in Google ADK

Q: How do I register the Armano MCP Server with Google ADK?

Use McpToolset with your Vinkius HTTP URL to load the tools. Pass this toolset into your LlmAgent tools parameter so your Gemini model can call tools like createtimeentry natively.

Connect Armano to Google ADK to analyze employee timesheets and project data using Gemini's million-token context.

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Connect Armano MCP to Google ADK

Create your Vinkius account to connect Armano 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 Armano with Google ADK

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Query Armano MCP Server data inside Google ADK

The `list_time_entries` and `list_projects` tools feed timesheet data directly into your Gemini agent. The ADK lets you combine this operational data with your historical records in BigQuery, making it easy to run deep resource analysis. Because Gemini handles massive context windows, your agent can ingest months of time entries at once. It uses these tools to cross-reference active projects with actual hours worked, flagging budget overruns directly in your Google Cloud environment.

Manage employee leave and department tracking

The `list_employees`, `list_departments`, and `list_leaves` tools expose your organizational structure to the Gemini ecosystem. Your agent queries these tools to map out department availability and track active leave requests. By running this MCP Server inside your Google Cloud project, you can build autonomous HR workflows. The agent checks employee availability and automatically updates team calendars or Vertex AI pipelines based on who is out of the office.

Real-time status checks and project tracking

The `check_armano_status` and `get_project` tools verify connection health and retrieve active project parameters. Your agent uses these endpoints to ensure your data pipeline is active before executing complex analytical queries. If the status check returns a healthy connection, the agent proceeds to pull project details. This prevents execution failures in your automated Vertex AI pipelines by ensuring the target API is responsive before processing.

Setup guide

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

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Common questions about Armano MCP in Google ADK

Use `McpToolset` with your Vinkius HTTP URL to load the tools. Pass this toolset into your `LlmAgent` tools parameter so your Gemini model can call tools like `create_time_entry` natively.

Yes, you can use the optional tool names filter in the ADK configuration. This prevents Gemini from accessing tools like `list_employees` if you only want it managing project timesheets.

Gemini's long-context capabilities allow the agent to process large lists of time entries from `list_time_entries` without hitting token limits. The ADK handles the data stream directly from the server transport.

Yes, you can run the server via HTTP or Stdio transports. This allows you to test your time-tracking agents locally before deploying them to Vertex AI or Google Cloud Run.

Vinkius processes all API requests in a zero-trust, ephemeral sandbox. Your employee details and leave records are decrypted only in memory during execution and are never written to persistent storage.

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