How to Use the Fusion Operations MCP in Google ADK

Q: Can I filter tools when using Fusion Operations with Google ADK?

You can use the toolnames filter to restrict which functions your agent can access. This keeps your agent focused on specific tasks like order creation.

Q: How does Google ADK handle the long-context needs of Fusion Operations?

The Gemini models process the output of listproductionrecords directly. This allows for deep reasoning over long histories of factory performance.

Link Fusion Operations to Google ADK to drive enterprise manufacturing logic using Gemini's long-context reasoning.

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Connect Fusion Operations MCP to Google ADK

Create your Vinkius account to connect Fusion Operations 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 Fusion Operations with Google ADK

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BigQuery-ready manufacturing data

Pull data from `list_manufacturing_products` into your Google ADK workflow to align your factory output with your cloud-hosted business metrics. This connects your physical floor state to your existing Google Cloud infrastructure. Your agents can now reason over millions of tokens of production history. By feeding `list_production_records` into your Gemini models, you identify trends that were previously buried in the noise.

Granular floor scheduling

Use `list_floor_workers` to map available labor against your current `list_production_orders`. This allows your Google ADK agent to allocate resources based on shift availability rather than static estimates. Keep your operations tight by verifying worker status before finalizing any new orders. The integration ensures your agent understands the human element of your factory floor.

Multi-site factory control

Run `get_manufacturing_site_info` to switch between different production facilities seamlessly. Your Google ADK setup manages these contexts, ensuring your agent acts on the correct site data every time. This works for complex organizations managing multiple locations. You get a unified view of your entire manufacturing footprint through one interface.

Setup guide

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

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Common questions about Fusion Operations MCP in Google ADK

You initialize a McpToolset using your server URL and pass it to your LlmAgent. It connects directly, giving your agents access to your floor data.

It does. You can use the toolset alongside your existing BigQuery data sources. Your agent will have full context of both your cloud and floor data.

You can use the tool_names filter to restrict which functions your agent can access. This keeps your agent focused on specific tasks like order creation.

The Gemini models process the output of `list_production_records` directly. This allows for deep reasoning over long histories of factory performance.

We enforce strict endpoint token authentication for all requests. Your current inventory stocks and site details are kept in an ephemeral, isolated environment.

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