AgroLog MCP Server for Cursor 11 tools — connect in under 2 minutes
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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{
"mcpServers": {
"agrolog": {
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
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}
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About AgroLog MCP Server
Connect your AgroLog Grain Monitoring API to any AI agent and take full control of real-time temperature monitoring, moisture tracking, CO2 spoilage detection, crop level inventory, and automated aeration control through natural conversation.
Cursor's Agent mode turns AgroLog into an in-editor superpower. Ask Cursor to generate code using live data from AgroLog and it fetches, processes, and writes. all in a single agentic loop. 11 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.
What you can do
- Temperature Monitoring — Get real-time grain temperature readings from sensors in silos and bins
- Moisture Tracking — Monitor grain moisture content for safe storage and drying decisions
- CO2 Detection — Detect elevated CO2 levels as early warning signs of spoilage and mold growth
- Crop Level Inventory — Track grain volume and silo fill levels for inventory management
- Weather Station Data — Access outdoor temperature, humidity, wind speed, and rainfall data
- Device Management — List all monitoring devices and view their configuration attributes
- Relay Control — Remotely control fans, aeration systems, and dryers connected to AgroLog devices
- Alarm Monitoring — Track active alarms and alerts for proactive grain management
- Historical Telemetry — Retrieve time-series sensor data for trend analysis and reporting
- Multi-Customer Management — Manage devices across multiple farms or customer organizations
The AgroLog MCP Server exposes 11 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 AgroLog to Cursor via MCP
Follow these steps to integrate the AgroLog 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 AgroLog
Open Agent mode in chat and ask: "Using AgroLog, help me...". 11 tools available
Why Use Cursor with the AgroLog MCP Server
Cursor AI Code Editor provides unique advantages when paired with AgroLog 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
AgroLog + Cursor Use Cases
Practical scenarios where Cursor combined with the AgroLog 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
AgroLog MCP Tools for Cursor (11)
These 11 tools become available when you connect AgroLog to Cursor via MCP:
get_alarms
Alarms are triggered by threshold breaches (high temperature, high moisture, elevated CO2, equipment failure) and indicate conditions requiring immediate attention. Returns alarm severity (critical, warning, info), alarm type, affected device, timestamp, and acknowledgment status. Essential for proactive grain management, quality issue detection, and operational response. AI agents should use this when users ask "show me all active alarms", "what alerts have been triggered", or need alarm data for operational monitoring. Optional device_id filters alarms for a specific device. Get active and historical alarms/alerts from the AgroLog monitoring system
get_co2
Elevated CO2 levels indicate biological activity (mold growth, insect respiration, or grain respiration) and are early warning signs of spoilage before temperature changes become apparent. Returns timestamped CO2 value in ppm. Essential for early spoilage detection, grain quality monitoring, and proactive storage management. AI agents should use this when users ask "what is the CO2 level in silo 2", "check headspace gas readings for device X", or need early warning indicators of grain spoilage. Get CO2/headspace gas readings from a specific monitoring device
get_crop_level
Crop level sensors measure the grain volume or height in silos and bins, enabling inventory management and capacity planning. Returns timestamped crop level value (percentage or distance). Essential for grain inventory tracking, bin capacity management, and logistics planning. AI agents should reference this when users ask "how full is silo 4", "check crop level for device X", or need inventory data for storage management and logistics planning. Get grain crop level (volume/quantity) readings from a specific monitoring device
get_customer_devices
Returns device IDs, names, types, and status for the specified customer. Essential for multi-farm management, service provider operations, and organizational device administration. AI agents should use this when users ask "show me all devices for customer X", "list sensors for this farm organization", or need customer-scoped device inventory in multi-tenant deployments. List all monitoring devices for a specific customer/organization in multi-tenant setups
get_device_attributes
Essential for understanding device setup, sensor positioning within silos, and device management. AI agents should reference this when users ask "show me the configuration for this sensor", "what is the calibration data for device X", or need device metadata for system administration. Get configuration attributes and metadata for a specific monitoring device
get_device_telemetry
Supports custom key selection (temperature, moisture, co2, humidity, etc.) and configurable data point limits for historical analysis. Essential for trend analysis, condition monitoring over time, and creating data visualizations. AI agents should reference this when users ask "show me temperature history for device X over the last 48 hours", "get moisture trend for this sensor", or need historical telemetry data for grain management analysis. Get time-series telemetry data from a specific monitoring device with customizable keys and limits
get_devices
Returns device IDs, names, types (temperature sensor, moisture sensor, weather station, crop level monitor, headspace/CO2 sensor), labels, and current status. Essential for device inventory, system overview, and selecting specific sensors for telemetry queries. AI agents should use this when users ask "show me all sensors in my grain silo", "list monitoring devices", or need to identify available devices before querying temperature, moisture, or other telemetry data. List all AgroLog monitoring devices (temperature, moisture, weather sensors) in your system
get_moisture
Moisture content is the most critical factor for safe grain storage — high moisture leads to mold, spoilage, and heating. Returns timestamped moisture value as percentage. Essential for grain quality assessment, drying decisions, and storage safety monitoring. AI agents should reference this when users ask "what is the moisture level in bin 5", "check grain moisture for device X", or need moisture data for storage management and drying planning. Get current grain moisture readings from a specific monitoring device
get_temperature
Temperature is critical for detecting spoilage, mold growth, and insect activity in stored grain. Returns timestamped temperature value in Celsius. Essential for grain quality monitoring, spoilage prevention, and ventilation scheduling. AI agents should use this when users ask "what is the temperature in silo 3", "check grain temperature for device X", or need current temperature data for storage management decisions. Device IDs can be found using get_devices. Get current grain temperature readings from a specific monitoring device
get_weather
Essential for drying decisions (outdoor air conditions for natural air drying), harvest planning (rain forecasts, wind conditions), and understanding environmental impact on stored grain. Returns the latest 10 readings with timestamps. AI agents should use this when users ask "what are the current weather conditions at my facility", "show me wind speed and rainfall data", or need weather context for grain management decisions. Get weather station data (temperature, humidity, wind, rainfall) from a specific device
set_relay_state
Accepts device ID, relay name, and desired state (true=on, false=off). Essential for remote grain management, automated ventilation scheduling, and responding to temperature/moisture alerts. AI agents should use this when users ask "turn on the fan for silo 3", "activate aeration for bin 2", or need to remotely control ventilation equipment based on sensor readings. WARNING: Always verify current conditions before changing relay states. Control relay outputs (fans, aeration, dryers) connected to an AgroLog device
Example Prompts for AgroLog in Cursor
Ready-to-use prompts you can give your Cursor agent to start working with AgroLog immediately.
"Check the temperature and moisture in silo 3 and tell me if there is any spoilage risk."
"Show me all active alarms in my grain storage facility."
"What is the current crop level inventory across all my grain bins?"
Troubleshooting AgroLog MCP Server with Cursor
Common issues when connecting AgroLog to Cursor through the Vinkius, and how to resolve them.
Tools not appearing in Cursor
Server shows as disconnected
AgroLog + Cursor FAQ
Common questions about integrating AgroLog MCP Server with Cursor.
What is Agent mode and why does it matter for MCP?
Where does Cursor store MCP configuration?
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?
How do I verify MCP tools are loaded?
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Connect AgroLog to Cursor
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.