How to Use the Wiagro MCP in Mastra AI

Q: Can I use Mastra AI to track long-term humidity trends?

Yes. You can set up a multi-day workflow using gethumidityhistory. This tracks moisture migration and condensation risks over time, making it easy to spot gradual quality deterioration.

Q: What happens if the sensor data is incomplete when using Wiagro with Mastra AI?

The agent handles this by flagging missing data points. If getsensorhealth indicates multiple sensors are offline, the workflow can stop and notify an admin instead of generating a faulty report.

Q: Does Wiagro support checking external factors when using Mastra AI?

It does. By combining getsatellitedata with getqualityassessment, your workflow can assess both internal grain conditions and external environmental risks simultaneously.

Build complex failure-resistant workflows handling Wiagro data with Mastra AI.

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Connect Wiagro MCP to Mastra AI

Create your Vinkius account to connect Wiagro 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 Wiagro with Mastra AI

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Perplexity

Handling Complex Spoilage Detection

The `get_temperature_history` tool is key for tracking spoilage. Since temperature spikes indicate mold or insect activity, a workflow can check this history and then branch: if the temp rises above X, initiate an alert sequence. Mastra AI’s ability to automatically retry failed steps means your agent doesn't crash just because one sensor reading times out. It handles failures gracefully while checking historical temperature data.

Multi-Step Quality Assessment Workflows

Need a full assessment? A workflow can first call `get_silobag_details` to get context, then use `get_quality_assessment` for the score. If the risk level is 'High,' it triggers a second step: calling `get_rupture_alerts` to check for structural damage. This conditional branching makes your process powerful. You can build workflows that only proceed if all necessary data points—like current CO2 levels via `get_current_readings`—are available.

Facility-Wide Monitoring and Reporting

For executive reporting, you need the big picture. The MCP Server provides this with `get_facility_overview`. You can build a workflow that runs this single command, aggregates data from multiple silobags, and then formats it into a report. If the overall quality score is low, the Mastra AI agent automatically executes secondary actions, like requesting `get_satellite_data` to check external weather risks.

Setup guide

Set up Wiagro 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 Wiagro 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: "wiagro-mcp-client",
  servers: {
    "wiagro-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

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

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Common questions about Wiagro MCP in Mastra AI

Mastra AI's built-in retries are perfect here. If the connection momentarily drops while calling `get_alerts`, the workflow automatically attempts to re-fetch the critical alert status, ensuring no manual intervention is needed.

Yes. You can set up a multi-day workflow using `get_humidity_history`. This tracks moisture migration and condensation risks over time, making it easy to spot gradual quality deterioration.

The agent handles this by flagging missing data points. If `get_sensor_health` indicates multiple sensors are offline, the workflow can stop and notify an admin instead of generating a faulty report.

It does. By combining `get_satellite_data` with `get_quality_assessment`, your workflow can assess both internal grain conditions and external environmental risks simultaneously.

This server touches sensor readings (temperature, CO2, humidity), quality scores, and silobag metadata. The system ensures all operational monitoring is handled securely via the MCP Server.

Use it with your favorite AI tools

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