How to Use the GrainSure MCP in Mastra AI

Q: Can Mastra AI automatically retry a failed GrainSure delivery order?

Yes. You can wrap createdeliveryorder in a Mastra workflow that catches network timeouts and retries the call using exponential backoff.

Q: How do I enforce human approval for GrainSure MCP actions in Mastra AI?

Use the requireToolApproval setting on your agent. This pauses the execution of createdeliveryorder until a human operator confirms the volume.

Automate grain replenishment workflows using Mastra AI and the GrainSure MCP Server.

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Create your Vinkius account to connect GrainSure 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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Trigger Automated Mastra AI Replenishment Workflows

`get_low_stock_alerts` acts as the initial trigger for your automated supply chain pipelines. Mastra AI processes this alert, evaluates the silo's remaining capacity, and prepares to restock without human intervention. If the initial request fails, the workflow engine retries the order with exponential backoff. Your agent relies on `get_silo_details` to verify the exact grain type before executing any external transactions.

Human-in-the-Loop Delivery Approvals

This MCP tool schedules a fresh shipment of grain based on real-time deficit calculations. Mastra AI halts this specific step, requiring an operator's manual approval via Slack before final submission. The agent presents the supervisor with the output from `get_days_to_empty` to justify the order. Once approved, the workflow resumes and logs the confirmation.

Continuous Sensor Maintenance Pipelines

`get_sensor_health` monitors battery levels and calibration status across all your farm hardware. Your Mastra AI agent runs scheduled checks, flagging offline units to the maintenance crew. The workflow automatically schedules a calibration task when a sensor reports poor health. It uses `get_silos` to map the exact physical location of the malfunctioning unit.

Setup guide

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

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

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

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Common questions about GrainSure MCP in Mastra AI

Instantiate the client with your Vinkius URL, then fetch the tool definitions using `listTools`. Spread these directly into your agent's tool array to grant it instant silo access.

Yes. You can wrap `create_delivery_order` in a Mastra workflow that catches network timeouts and retries the call using exponential backoff.

Use the `requireToolApproval` setting on your agent. This pauses the execution of `create_delivery_order` until a human operator confirms the volume.

Yes. The client automatically detects the transport type, allowing you to connect via SSE or Streamable HTTP without changing your core workflow code.

Your coordinates and order data run within a secure local container. No external servers store your operational metrics.

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