Can my AI predict when my silo will run empty based on current usage?

Yes! Use the `get_days_to_empty` tool with your silo ID. GrainSure AI analyzes current fill levels and historical usage patterns to predict exactly how many days until the silo runs empty, with a confidence score. For deeper analysis, combine with `get_usage_history` to see the consumption trends that drive the prediction. This gives you proactive warning to schedule deliveries before running out.

How do I set up low stock alerts for my silos?

Use the `update_silo_settings` tool to configure your low stock threshold percentage (e.g., 20% means alert when silo drops below 20% full). Then use `get_low_stock_alerts` to check for any active alerts. GrainSure will automatically monitor fill levels and trigger alerts when thresholds are breached, giving you timely warning to plan deliveries.

Can I create a delivery order directly through the API?

Yes! Use the `create_delivery_order` tool with the silo ID, quantity in tonnes, and optionally a preferred delivery date and supplier. The API will confirm your order with an order ID and estimated delivery date. You can also use `get_days_to_empty` predictions to automatically trigger orders when silos reach critical levels.

GrainSure MCP Server for Mastra AI 12 tools — connect in under 2 minutes

Name: GrainSure
Availability: InStock
Author: Vinkius

docs.mastra.ai/docs/agents/mcp-tools

Built by Vinkius GDPR 12 Tools SDK

Mastra AI is a TypeScript-native agent framework built for modern web stacks. Connect GrainSure through Vinkius and Mastra agents discover all tools automatically. type-safe, streaming-ready, and deployable anywhere Node.js runs.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

typescript

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

async function main() {
  // Your Vinkius token. get it at cloud.vinkius.com
  const mcpClient = await createMCPClient({
    servers: {
      "grainsure": {
        url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
      },
    },
  });

  const tools = await mcpClient.getTools();
  const agent = new Agent({
    name: "GrainSure Agent",
    instructions:
      "You help users interact with GrainSure " +
      "using 12 tools.",
    model: openai("gpt-4o"),
    tools,
  });

  const result = await agent.generate(
    "What can I do with GrainSure?"
  );
  console.log(result.text);
}

main();

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About GrainSure MCP Server

Connect your GrainSure Silo Monitoring API to any AI agent and take full control of real-time grain fill level tracking, usage rate analysis, predictive days-to-empty forecasting, and automated delivery management through natural conversation.

Mastra's agent abstraction provides a clean separation between LLM logic and GrainSure tool infrastructure. Connect 12 tools through Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution. deployable to any Node.js host in one command.

What you can do

Silo Management — List and manage all grain silos with current fill levels, grain types, and monitoring status
Real-Time Fill Levels — Get current grain fill percentage and remaining tonnes for each silo
Usage Tracking — Monitor historical grain consumption rates and identify usage trends
Days to Empty — Get AI-predicted days until each silo runs empty based on current usage patterns
Fill Level History — Track how fill levels have changed over time for delivery effectiveness analysis
Low Stock Alerts — Receive automated alerts when silo levels drop below configured thresholds
Delivery Orders — Create and manage grain delivery orders for timely inventory replenishment
Order History — Track past deliveries, quantities, and supplier performance
Sensor Health — Monitor IoT sensor battery levels, signal strength, and calibration status
Farm Overview — Get comprehensive farm-wide inventory summaries for executive reporting
Silo Settings — Customize alert thresholds, grain types, and usage rate assumptions

The GrainSure MCP Server exposes 12 tools through the Vinkius. Connect it to Mastra AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect GrainSure to Mastra AI via MCP

Follow these steps to integrate the GrainSure MCP Server with Mastra AI.

Install dependencies

Run npm install @mastra/core @mastra/mcp @ai-sdk/openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.ts and run with npx tsx agent.ts

Explore tools

Mastra discovers 12 tools from GrainSure via MCP

Why Use Mastra AI with the GrainSure MCP Server

Mastra AI provides unique advantages when paired with GrainSure through the Model Context Protocol.

Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure. add GrainSure without touching business code

Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation

TypeScript-native: full type inference for every GrainSure tool response with IDE autocomplete and compile-time checks

One-command deployment to any Node.js host. Vercel, Railway, Fly.io, or your own infrastructure

GrainSure + Mastra AI Use Cases

Practical scenarios where Mastra AI combined with the GrainSure MCP Server delivers measurable value.

Automated workflows: build multi-step agents that query GrainSure, process results, and trigger downstream actions in a typed pipeline

SaaS integrations: embed GrainSure as a first-class tool in your product's AI features with Mastra's clean agent API

Background jobs: schedule Mastra agents to query GrainSure on a cron and store results in your database automatically

Multi-agent systems: create specialist agents that collaborate using GrainSure tools alongside other MCP servers

GrainSure MCP Tools for Mastra AI (12)

These 12 tools become available when you connect GrainSure to Mastra AI via MCP:

create_delivery_order

Accepts delivery quantity (tonnes), preferred delivery date, supplier information, and any special instructions. Returns order confirmation with order ID, estimated delivery date, and tracking information. Essential for proactive inventory replenishment, automated ordering based on predictions, and supply chain management. AI agents should use this when users ask "order 20 tonnes of wheat for silo 3", "schedule a delivery for silo 5 next week", or need to place delivery orders based on low stock predictions. Create a new grain delivery order for a specific silo

get_current_level

Returns fill percentage, remaining tonnes, current level height, and last update timestamp. Essential for real-time inventory tracking, delivery planning, and stock management. AI agents should use this when users ask "what is the current fill level in silo 2", "how much grain is left in silo 4", or need immediate stock level data for feed planning and delivery decisions. Get real-time grain fill level for a specific silo

get_days_to_empty

Returns estimated days to empty, predicted empty date, confidence score, and usage rate assumptions. Essential for proactive delivery planning, preventing stock-outs, and optimizing supply chain timing. AI agents should use this when users ask "when will silo 3 run empty", "how many days of feed are left in silo 5", or need predictive supply data for delivery scheduling. Get AI-predicted days until a silo runs empty based on current usage patterns

get_farm_overview

Essential for executive reporting, farm-wide inventory assessment, and strategic supply planning. AI agents should use this when users ask "give me an overview of all my silos", "what is the total grain inventory across the farm", or need farm-level summaries for management reporting. Get comprehensive overview of all monitored silos on the farm

get_fill_level_history

Returns time-series fill percentage data with timestamps showing how stock levels have changed over time. Essential for fill trend analysis, delivery effectiveness assessment, and consumption pattern identification. AI agents should use this when users ask "show me fill level trends for silo 1 over the past 60 days", "has silo 2 been filling or depleting", or need historical fill data for inventory management. Optional days parameter controls lookback period. Get historical fill level readings for a specific silo

get_low_stock_alerts

Returns alert severity (critical, warning, info), affected silo, current fill percentage, threshold level, timestamp, and recommended actions. Essential for proactive inventory management, preventing stock-outs, and timely delivery ordering. AI agents should use this when users ask "show me all low stock alerts", "is silo 3 running low", or need alert data for inventory monitoring. Optional silo_id filters alerts for a specific silo. Get low stock alerts for silos or a specific silo

get_order_history

Essential for delivery tracking, supplier performance assessment, and inventory replenishment planning. AI agents should reference this when users ask "show me delivery history for silo 2", "when was the last delivery to silo 4", or need order data for supply chain analysis. Get delivery order history for a specific silo

get_sensor_health

Returns sensor battery level, signal strength, last communication time, calibration status, and operational status (active, low battery, offline, needs calibration). Essential for sensor maintenance, data continuity assurance, and monitoring system reliability. AI agents should reference this when users ask "is the sensor working in silo 5", "does silo 3 need sensor calibration", or need sensor health data for system administration. Get health status of the level monitoring sensor for a specific silo

get_silo_details

Essential for understanding silo context before analyzing usage data, planning deliveries, or generating inventory reports. AI agents should reference this when users ask "tell me about silo 3", "what grain is stored in silo 5", or need detailed silo metadata for informed decisions. Get detailed information about a specific grain silo

get_silos

Returns silo IDs, names, locations, grain types, current fill levels, and monitoring status. Essential for farm overview, silo inventory management, and selecting specific silos for detailed analysis. AI agents should use this when users ask "show me all my silos", "list monitored storage units", or need to identify available silos before querying fill levels or usage data. List all grain silos monitored by GrainSure

get_usage_history

Returns time-series usage data (tonnes per day/week) with timestamps. Essential for consumption trend analysis, feed rate calculation, and delivery timing optimization. AI agents should reference this when users ask "show me grain usage trends for silo 3", "what is the daily consumption rate for silo 5", or need historical usage data for feed planning and inventory forecasting. Optional days parameter controls lookback period. Get historical grain usage data for a specific silo

update_silo_settings

Essential for customizing monitoring behavior, adjusting alert sensitivity, and maintaining accurate silo profiles. AI agents should use this when users ask "change the low stock threshold for silo 3 to 20 percent", "update silo 5 grain type to barley", or need to modify silo monitoring configuration. Update silo monitoring settings including alert thresholds and grain type