How to Use the Centaur Analytics MCP in Mastra AI

Q: How do I add Centaur Analytics tools to a Mastra AI agent?

You initialize the client, call listTools() to retrieve the grain monitoring capabilities, and spread them directly into your agent's tool configuration. The agent can then call tools like getbins autonomously.

Q: Can Mastra AI handle human-in-the-loop approvals for Centaur Analytics actions?

Yes, you can set the requireToolApproval flag before executing a tool like getalerts. This ensures an operator confirms the warning status before the workflow triggers automated aeration.

Build automated grain aeration workflows and spoilage alerts using Mastra AI and Centaur Analytics.

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Connect Centaur Analytics MCP to Mastra AI

Create your Vinkius account to connect Centaur Analytics to Mastra AI 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 Mastra AI

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Automating Aeration with Mastra AI MCP Server

`get_current_readings` fetches live moisture and temperature metrics to trigger Mastra AI workflow steps based on physical bin conditions. The framework evaluates these sensor values against your local thresholds, deciding whether to run fans or log warnings. If moisture levels spike, the workflow uses `get_alerts` to check for active warnings before executing a fan cycle. This programmatic loop prevents unnecessary aeration runs while ensuring grain stays dry.

Multi-Step Spoilage Prevention

`get_spoilage_predictions` calculates the risk level and days remaining before grain quality degrades inside a specific silo. Your Mastra AI agent monitors these predictions on a cron schedule, using the MCP tool to automatically escalate critical risks to your team. When risk levels cross your threshold, the agent calls `get_quality_report` to compile a complete condition assessment. It then forwards this data to your notification step, giving operators the full context needed to prevent spoilage.

Historical Trend Analysis

`get_co2_history` retrieves historical carbon dioxide levels over a user-defined lookback period to track biological activity. Your Mastra AI agent uses this data to map out trend lines and identify slow-burning problems that real-time checks miss. The agent pairs this historical view with `get_temperature_history` to verify if rising CO2 correlates with localized hot spots. Combining these metrics gives you a clear picture of biological activity inside the grain mass.

Setup guide

Set up Centaur Analytics MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Centaur Analytics tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "centaur-analytics-mcp-client",
  servers: {
    "centaur-analytics-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Centaur Analytics Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Centaur Analytics tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Centaur Analytics transactions"
);
console.log(result.text);

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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 Mastra AI

You initialize the client, call `listTools()` to retrieve the grain monitoring capabilities, and spread them directly into your agent's tool configuration. The agent can then call tools like `get_bins` autonomously.

Yes, you can set the `requireToolApproval` flag before executing a tool like `get_alerts`. This ensures an operator confirms the warning status before the workflow triggers automated aeration.

The framework auto-detects the transport type, supporting both Server-Sent Events and HTTP. This allows your workflows to communicate reliably with the grain sensors hosted on Vinkius.

The built-in workflow engine uses automatic retries with exponential backoff. If a heavy query like `get_moisture_history` encounters a network hiccup, the system retries without failing the entire run.

All telemetry data, including CO2 histories and temperature readings, stays within Vinkius's zero-trust MCP sandbox. Your Mastra AI workflows only access the data via secure, authenticated endpoints, preventing unauthorized exposure of your facility metrics.

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