AgroLog MCP. Manage grain conditions and climate control.
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AgroLog monitors stored grain conditions in real time, letting you check temperature, moisture content, CO2 spoilage levels, and silo fill amounts from any AI agent.
You can also remotely activate or deactivate aeration fans and dryers. Use this MCP to manage entire storage facilities—from reading current sensor readings to pulling historical trends for quality reports.
What your AI agents can do
Get alarms
This tool retrieves all active and historical alerts from the system, telling you what conditions require immediate attention.
Get co2
It provides time-stamped CO2 readings to detect early signs of spoilage or mold growth in stored grain headspace.
Get crop level
This fetches the current volume percentage or distance reading for a specific silo, helping you manage inventory capacity.
The system instantly lists all active alerts, like high temperatures or critical moisture drops, so you know what needs fixing right now.
It provides current CO2 and temperature readings to tell you if mold growth or biological activity is starting before other signs appear.
You can command the AI agent to turn fans, dryers, or blowers on or off based on real-time environmental needs.
The system reads current crop levels across all bins, giving you an accurate count of stored volume for logistics planning.
Get time-series data for moisture or temperature spanning weeks, which is key for insurance reports and quality audits.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
AgroLog: 11 Monitoring Tools
Use these tools to check specific sensor readings, retrieve historical data streams, or manage physical controls within your grain storage facility.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using AgroLog on Vinkius019d7549get alarms
This tool retrieves all active and historical alerts from the system, telling you what conditions require immediate attention.
019d7549get co2
It provides time-stamped CO2 readings to detect early signs of spoilage or mold growth in stored grain headspace.
019d7549get crop level
This fetches the current volume percentage or distance reading for a specific silo, helping you manage inventory capacity.
019d7549get customer devices
It lists all monitoring devices associated with your organization ID in multi-farm setups.
019d7549get device attributes
This fetches the setup details and configuration metadata for any specific sensor or device.
019d7549get device telemetry
It retrieves customizable, time-series data streams (like temperature or moisture) from a device over specified periods.
019d7549get devices
This provides a comprehensive list of all connected monitoring devices, including their type and current status.
019d7549get moisture
It returns the current grain moisture content percentage from a specific sensor reading.
019d7549get temperature
This gets the real-time temperature reading in Celsius for any monitored silo or bin.
019d7549get weather
It pulls the latest local weather data, including wind speed and rainfall, for external planning.
019d7549set relay state
You can use this to remotely switch physical equipment like fans or aeration systems on or off.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
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- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with AgroLog, then connect any of our 4,800+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,800+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AgroLog. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
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Zero-Trust Proxy
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 11 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Checking grain storage used to require a whole lot of manual work.
Before this MCP, checking on your stored grain meant physically walking through facilities or logging into dozens of separate vendor dashboards. You'd open the temperature tab for Silo 3, switch over to the moisture tracker for Bin 2, then cross-reference that data with a weather report from another site. Copying and pasting those values into an Excel sheet just to know if you had a spoilage risk was a multi-hour task.
Now, your AI agent handles the whole workflow. You simply ask, 'Give me an assessment of Silo 7.' The MCP pulls in current temperature data using `get_temperature`, checks for elevated CO2 using `get_co2`, and verifies if any critical alerts are active with `get_alarms`. You get a single, actionable summary, not twelve different screens.
Controlling the environment with AgroLog MCP
The most tedious part used to be that if you saw a temperature spike, you had to manually log into a separate control interface, find the fan relay for that specific bin, and hit 'ON.' This was slow, prone to human error, and often delayed critical action.
With this MCP, your AI client executes `set_relay_state` directly based on sensor readings. Your agent sees high temperature via `get_temperature`, determines the need, and turns the fan on—all without you touching a physical switch or another dashboard.
What you can do with this MCP connector
Running a grain operation means managing complex variables: temperature spikes, moisture creep, CO2 buildup, and accurate inventory counts. With this connector, you talk to your AI agent about the silo's condition just like talking to a floor manager. Your agent automatically pulls readings from multiple sensors—checking current temperatures or detecting elevated CO2 levels—and tells you if things are okay.
You can also pull historical data streams to see how conditions changed over weeks. Need to fix an issue? The system lets your AI agent remotely control the aeration fans and dryers based on what the sensors report. Because this MCP is hosted in the Vinkius catalog, it connects your existing workflow from any compatible client, making complex facility management conversational.
019d7549-d91d-7302-b4b0-66bea760074c 
How AgroLog MCP Works
- 1 First, you connect your AgroLog credentials (username, password, URL) to the MCP in Vinkius.
- 2 Next, you ask your AI agent a question—like 'What's wrong with Silo 5?'—and it calls the relevant tools automatically.
- 3 Finally, you get a plain language summary from your agent that tells you exactly what the sensors found and what action to take.
The bottom line is this MCP turns complicated sensor data into simple conversations with your AI client.
Who Is AgroLog MCP For?
This MCP is for facility managers, grain elevator operators, and agricultural consultants who spend too much time switching between silo dashboards to check on the health of stored crops. If you're tired of manual inspections or digging through endless logs, this tool gives your AI agent a single point of truth.
They use it to monitor temperature and moisture across dozens of bins simultaneously, ensuring nothing spoils before the next shipment leaves.
They manage device health by listing all connected sensors using get_devices and checking their configuration attributes via get_device_attributes.
They run historical telemetry queries to build data-driven reports for clients, recommending changes based on past CO2 or temperature trends.
What Changes When You Connect
- Stop guessing about storage health. Instead of manual checks, use
get_alarmsto get a single list of everything that's critically wrong across all bins. - Save time on reports. Don't manually grab data from different screens; ask your agent for historical telemetry using
get_device_telemetryand get trend lines instantly. - Control the environment without being there. When moisture levels rise, immediately tell your AI client to run
set_relay_stateand activate aeration systems. - Know what you have on hand. Use
get_crop_levelto check inventory across every silo, making capacity planning a simple query instead of a dashboard drill-down. - Be proactive about spoilage. Check for early indicators by running
get_co2alongsideget_temperaturereadings; this combination lets you catch mold issues weeks before they become visible.
Real-World Use Cases
Handling a potential outbreak
A consultant asks, 'What's happening in Silo 4?' Your agent first calls get_alarms to see if anything is critical. Then it checks get_co2 and get_temperature. If both are high, the agent summarizes: 'Spoilage risk detected; activate aeration immediately using set_relay_state.'
Pre-shipment inventory check
A manager needs to know total available volume. They query the system which uses get_crop_level for every bin and sums them up, providing a clear tonnage estimate.
Planning natural drying cycles
Before sending grain out, the agent checks get_weather data to forecast rainfall or wind speed. This helps determine if outdoor air conditions are good enough for drying before manually adjusting controls using set_relay_state.
Multi-site audit
An operator needs an inventory list across three different farms. They use get_customer_devices to pull device IDs for each site, then call get_device_telemetry for comparative trend analysis.
The Tradeoffs
Treating it like a simple dashboard
Trying to figure out the full picture by just running 'Show me all data' on one screen. This only gives you snapshots, not actionable sequences.
→
You must chain calls. Start with get_devices to identify assets; then use get_temperature and get_moisture separately to get the necessary details for a full assessment.
Only checking current readings
Asking 'Is the moisture high?' today, but having no idea if it was trending up over the last week. You miss the critical rate of change.
→
Always pull time-series data using get_device_telemetry to see historical trends. This shows the rate of change, which is more important than any single reading.
Ignoring system alerts
Just looking at the sensor values without first running get_alarms. You might miss a critical alert that requires immediate action.
→
Always address potential failures by checking for alarms using get_alarms first. This tells you what needs attention before proceeding with normal monitoring.
When It Fits, When It Doesn't
Use this MCP if your core problem is managing physical, time-sensitive assets like silos and bins. You need to know what the sensor readings are (e.g., get_moisture, get_temperature) AND you need to act on them (via set_relay_state). Don't use it if you only need general data listing; for example, if you just need a list of all connected sensors, stick with get_devices. If your primary goal is financial reporting based purely on billing records, then an accounting MCP would be better. This tool is specifically about the physical health and inventory of agricultural goods.
Common Questions About AgroLog MCP
How do I check for spoilage risk using the get_co2 tool? +
The get_co2 tool gives current headspace gas readings. Elevated levels warn you of biological activity, indicating mold or insect respiration that is a key sign of spoilage.
Can I check inventory using the get_crop_level tool? +
Yes, get_crop_level provides the current grain volume percentage for any monitored silo, which is essential data for logistics and capacity planning.
What if I need to turn off a fan? Do I use set_relay_state? +
That's right. You use set_relay_state by specifying the device ID, relay name, and setting the desired state to 'false' (off).
How do I see all my equipment locations? +
You start by calling get_devices. This lists every sensor, weather station, or monitor connected to your system, giving you a full inventory.
What data can I get using the `get_device_telemetry` tool for trend analysis? +
The tool retrieves customizable, time-series sensor data. You specify keys—like temperature or moisture—and define a time range to analyze trends. This is vital for identifying subtle shifts in grain conditions over days or weeks.
If I need calibration or sensor positioning details, how do I use `get_device_attributes`? +
This tool returns critical metadata and configuration details for any specific device ID. You can validate setup parameters, check the last calibration date, or confirm a sensor's physical placement within the silo.
How can I use `get_customer_devices` to view inventory across multiple farms? +
It lists all monitoring equipment tied to your specific customer account or organization ID. This capability is essential when managing assets for a service provider or overseeing operations across different corporate sites.
What information does `get_weather` provide for harvest planning? +
The tool provides the latest readings on outdoor temperature, wind speed, rainfall, and humidity. You use this environmental context to make informed decisions about natural air drying or optimal harvest timing.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.