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 Cursor 12 tools — connect in under 2 minutes

Name: GrainSure
Availability: InStock
Author: Vinkius

docs.cursor.com/context/model-context-protocol

Built by Vinkius GDPR 12 Tools IDE

Cursor is an AI-first code editor built on VS Code that integrates LLM-powered coding assistance directly into the development workflow. Its Agent mode enables autonomous multi-step coding tasks, and MCP support lets agents access external data sources and APIs during code generation.

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Classic Setup·json

{
  "mcpServers": {
    "grainsure": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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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.

Cursor's Agent mode turns GrainSure into an in-editor superpower. Ask Cursor to generate code using live data from GrainSure and it fetches, processes, and writes. all in a single agentic loop. 12 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.

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 Cursor 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 Cursor via MCP

Follow these steps to integrate the GrainSure MCP Server with Cursor.

Open MCP Settings

Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"

Add the server config

Paste the JSON configuration above into the mcp.json file that opens

Save the file

Cursor will automatically detect the new MCP server

Start using GrainSure

Open Agent mode in chat and ask: "Using GrainSure, help me...". 12 tools available

Why Use Cursor with the GrainSure MCP Server

Cursor AI Code Editor provides unique advantages when paired with GrainSure through the Model Context Protocol.

Agent mode turns Cursor into an autonomous coding assistant that can read files, run commands, and call MCP tools without switching context

Cursor's Composer feature can generate entire files using real-time data fetched through MCP. no copy-pasting from external dashboards

MCP tools appear alongside built-in tools like file reading and terminal access, creating a unified agentic environment

VS Code extension compatibility means your existing workflow, keybindings, and extensions all work alongside MCP tools

GrainSure + Cursor Use Cases

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

Code generation with live data: ask Cursor to generate a security report module using live DNS and subdomain data fetched through MCP

Automated documentation: have Cursor query your API's tool schemas and generate TypeScript interfaces or OpenAPI specs automatically

Infrastructure-as-code: Cursor can fetch domain configurations and generate corresponding Terraform or CloudFormation templates

Test scaffolding: ask Cursor to pull real API responses via MCP and generate unit test fixtures from actual data

GrainSure MCP Tools for Cursor (12)

These 12 tools become available when you connect GrainSure to Cursor 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

Example Prompts for GrainSure in Cursor

Ready-to-use prompts you can give your Cursor agent to start working with GrainSure immediately.

"Show me the current fill levels for all my silos."

"How many days until my wheat silo runs empty?"

"Order 30 tonnes of barley for silo 2 with delivery next week."

Troubleshooting GrainSure MCP Server with Cursor

Common issues when connecting GrainSure to Cursor through the Vinkius, and how to resolve them.

Tools not appearing in Cursor

Ensure you are in Agent mode (not Ask mode). MCP tools only work in Agent mode.

Server shows as disconnected

Check Settings → Features → MCP and verify the server status. Try clicking the refresh button.

GrainSure + Cursor FAQ

Common questions about integrating GrainSure MCP Server with Cursor.

What is Agent mode and why does it matter for MCP?

Agent mode is Cursor's autonomous execution mode where the AI can perform multi-step tasks: reading files, editing code, running terminal commands, and calling MCP tools. Without Agent mode, Cursor operates in a simpler ask-and-answer mode that doesn't support tool calling. Always ensure you're in Agent mode when working with MCP servers.

Where does Cursor store MCP configuration?

Cursor looks for MCP server configurations in a mcp.json file. You can configure servers at the project level (.cursor/mcp.json in your project root) or globally (~/.cursor/mcp.json). Project-level configs take precedence.

Can Cursor use MCP tools in inline edits?

No. MCP tools are only available in Agent mode through the chat panel. Inline completions and Tab suggestions do not trigger MCP tool calls. This is by design. tool calls require user visibility and approval.

How do I verify MCP tools are loaded?

Open Settings → Features → MCP and look for your server name. A green indicator means the server is connected. You can also check Agent mode's available tools by clicking the tools dropdown in the chat panel.