How to Use the Centaur Analytics MCP in Google ADK

Q: Does Google ADK support filtering tools for Centaur Analytics?

You can use the toolnames filter to restrict which functions your agent can access. This keeps your agent's scope focused and secure.

Integrate Centaur Analytics with Google ADK to analyze grain quality trends using the vast reasoning capabilities of Gemini models.

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Connect Centaur Analytics MCP to Google ADK

Create your Vinkius account to connect Centaur Analytics 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 Centaur Analytics with Google ADK

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Granular bin analysis with Google ADK

Your agent uses `get_bin_details` and `get_bins` to map your entire storage layout. It builds a precise mental model of every silo in your facility. Once the agent knows the setup, it queries `get_current_readings` to pinpoint specific issues. It understands the relationship between sensor depth and grain health.

Sensor fleet management

Check the health of your hardware by calling `get_sensor_health`. Your agent identifies offline sensors or low-battery units before data gaps occur. This keeps your monitoring system reliable. Your agent acts as the primary interface for your maintenance team, reporting exactly which sensors need attention.

Historical quality assessment

Query `get_moisture_history` to track migration patterns over time. Your agent detects condensation issues that threaten your grain quality. It correlates this data with temperature trends to validate your aeration strategies. You get clear insights into why specific bins perform differently.

Setup guide

Set up Centaur Analytics 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 Centaur Analytics 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="Centaur Analytics_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Centaur Analytics 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 Centaur Analytics. 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 Centaur Analytics MCP in Google ADK

You connect the server via the McpToolset class. Your agent then gains access to all 12 tools for grain management.

Yes. You can use the agent to process large batches of bin data, perfect for enterprise-level grain storage oversight.

You can use the tool_names filter to restrict which functions your agent can access. This keeps your agent's scope focused and secure.

It returns structured data objects for every tool call. Your agent parses these directly into its reasoning process.

The server handles all authentication through a single endpoint token. Your sensor readings are never exposed to external logs or third-party systems.

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