Grain Watch MCP. Real-time silo condition monitoring and risk assessment.
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Grain Watch connects silo temperature monitoring to your AI client. Track real-time grain temperature, humidity levels, hot spots, and spoilage risk from any chat interface.
This server gives you immediate visibility into stored grain conditions across multiple silos.
What your AI agents can do
Get alerts
Returns all active alerts (temp, humidity, sensor) with severity and recommended actions for operational monitoring.
Get current humidity
Gets the current relative humidity percentage from multiple sensor positions in a silo to check for condensation risk.
Get current temperature
Retrieves live temperature readings (Celsius) from top, middle, bottom, and center zones throughout the grain mass.
Check live temperature and humidity levels across multiple zones within any monitored grain silo.
Run an overall assessment to determine the spoilage risk level, flagging critical areas across all storage units simultaneously.
Retrieve a list of active issues—like high temperatures or low humidity—along with recommended actions for immediate field response.
Pull time-series data for temperature or humidity to identify patterns, such as moisture migration over the last 30 days.
View the exact placement of every sensor (top, middle, bottom) and its corresponding depth within a specific silo structure.
Ask AI about this MCP
Supported MCP Clients
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Grain Watch: 12 Tools for Silo Condition Monitoring
Use these tools to query every facet of silo monitoring—from live temperature readings to historical trends and predictive spoilage risk.
019d75aaget alerts
Returns all active alerts (temp, humidity, sensor) with severity and recommended actions for operational monitoring.
019d75aaget current humidity
Gets the current relative humidity percentage from multiple sensor positions in a silo to check for condensation risk.
019d75aaget current temperature
Retrieves live temperature readings (Celsius) from top, middle, bottom, and center zones throughout the grain mass.
019d75aaget facility overview
Provides a high-level summary of all monitored silos' general status and average temperature for management reporting.
019d75aaget hotspot alerts
Finds specific, immediate alerts about localized heating (hot spots), which are early signs of potential spoilage issues.
019d75aaget humidity history
Retrieves time-series humidity data to track how moisture has moved or changed over a specific period.
019d75aaget sensor health
Checks the operational status, battery levels, and communication health of every sensor in a silo's network.
019d75aaget sensor map
Shows the physical location (depth/zone) for every installed sensor ID within a specific silo structure.
019d75aaget silo details
Gathers metadata about a single silo, such as its grain type and overall capacity, before running deeper analysis.
019d75aaget silos
Lists every monitored silo by name, ID, location, and general monitoring health status for inventory checks.
019d75aaget spoilage risk
Calculates an AI-powered risk score (low/moderate/high) for a specific silo based on current conditions and predicted spoilage time.
019d75aaget temperature history
Returns historical temperature data points over a given period, allowing detection of warming trends or effective cooling efforts.
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What you can do with this MCP connector
This server connects Grain Watch silo temperature monitoring straight to your AI client. You'll get full command over tracking real-time grain temp, humidity levels, hot spots, and spoilage risk right from any chat interface.
To start, you can list every monitored silo using get_silos, which gives you the name, ID, location, and general monitoring health status for quick inventory checks. Before running deeper analysis on a specific unit, call get_silo_details to gather metadata like its grain type and overall capacity.
When you need a high-level picture of your operation, run get_facility_overview; this provides management with a summary showing the general status and average temperature across all monitored silos. For immediate operational checks, get_silos lets you check live readings: use get_current_temperature to pull live temp data (in Celsius) from the top, middle, bottom, and center zones throughout any grain mass, while get_current_humidity gets the relative humidity percentage from multiple sensor positions to spot condensation risk.
If something's wrong right now, you get immediate alerts. You can retrieve all active issues—like high temperatures or low humidity—using get_alerts, which shows both the severity and what action you should take. For specific, localized heating problems, call get_hotspot_alerts; this finds immediate warnings about hot spots, which are early signs of potential spoilage.
You also need to know where your sensors are; run get_sensor_health to check the battery levels and communication status for every sensor in a silo's network, and use get_sensor_map to see the physical location (depth/zone) for each installed sensor ID.
For deep analysis, you track history. To spot warming trends or measure cooling efforts, pull historical temperature data points over time using get_temperature_history, or check moisture movement by getting time-series humidity data with get_humidity_history. When you combine current readings and predict the future, the server runs get_spoilage_risk, calculating an AI-powered risk score (low/moderate/high) for a specific silo based on its current conditions and predicted spoilage timeline.
Finally, if you need to understand what's going on with your equipment before running any checks, you can verify system operational status by calling get_sensor_health.
How Grain Watch MCP Works
- 1 Subscribe to this server on Vinkius. Then, enter your Grain Watch API key and base URL from the Grain Cloud dashboard.
- 2 Your AI client accesses the tools via natural language—you just ask: 'What is the spoilage risk for silo 3?'
- 3 The agent runs the necessary functions (like
get_spoilage_risk), collects the data, and formats a clear, actionable report.
The bottom line is you talk to your AI client like it's an on-site storage analyst that never sleeps.
Who Is Grain Watch MCP For?
This is for facility operators and consultants who are tired of manually checking dozens of silo dashboards. If you spend time coordinating maintenance teams based on scattered sensor data, this server is for you. It turns raw readings into a single action plan.
Manages temperature across hundreds of silos with automated alerts, needing to quickly check get_hotspot_alerts and coordinate immediate aeration.
Oversees the entire storage facility from one point. Needs high-level summaries using get_facility_overview to report overall status to executives.
Provides data-driven recommendations to clients by analyzing historical trends (get_temperature_history) and calculating long-term spoilage risk.
What Changes When You Connect
- Catch early spoilage with
get_hotspot_alerts. Instead of waiting for visible damage, the agent flags localized heating (like 7.2C above average) hours before it becomes critical. - Get facility context instantly using
get_facility_overview. You don't need to check 12 separate dashboards; you ask for an overview and get a consolidated status report immediately. - Analyze trends, not just snapshots. Running
get_temperature_historylets your agent plot temperature changes over weeks, which is critical for evaluating aeration effectiveness. - Reduce maintenance guesswork with
get_sensor_health. Before running any analysis, you check this tool to verify if a sensor's battery or connection status needs attention first. - Quantify risk using
get_spoilage_risk. The AI combines temp, humidity, and grain type into one score, telling you not just if there’s a problem, but how long you have to fix it.
Real-World Use Cases
Responding to an unknown warning
An operator gets an alert about 'Silo 7 has temperature issues.' Instead of guessing, the agent first runs get_silo_details to confirm it's corn. Then it uses get_hotspot_alerts to pinpoint the exact zone and finally runs get_spoilage_risk to tell them if they have hours or days before intervention is needed.
Routine weekly checkup
A consultant needs a full report for an audit. The agent first calls get_silos to list all units, then iterates through each silo using get_current_temperature, followed by get_humidity_history to build a comprehensive compliance report.
Investigating a weird temperature spike
A facility manager notices an unexplained temp jump. They ask the agent to run get_temperature_history for the last 30 days, which reveals the trend was gradual. They then compare this with get_sensor_map to ensure the sensor reporting the data is in the correct zone.
Initial site assessment
A new user needs to know what they have. The agent first runs get_silos for a list of all units, then uses that list to pull initial data using get_facility_overview before any deeper analysis is required.
The Tradeoffs
Checking only the current temperature
User asks: 'Is Silo 4 hot right now?' and gets a single temp reading. They assume everything is fine because it's below the threshold.
→
You need historical context. Ask for get_temperature_history to see if the temperature was trending up over the last week, or run get_hotspot_alerts which gives an immediate assessment of developing issues.
Ignoring sensor status
The AI agent reports high spoilage risk, but the user never checks the sensors. They waste time troubleshooting a problem that might be caused by bad data.
→
Always run get_sensor_health first. If the battery is low or the communication status is 'fault,' you know the reading isn't reliable and need to schedule maintenance instead.
Treating all silos equally
A user runs a general report but forgets which silo holds the high-risk grain. The resulting data mix makes it hard to prioritize action.
→
Use get_silo_details before any other tool on a specific unit. Confirm the grain type and context first. Then run get_spoilage_risk for an accurate, targeted assessment.
When It Fits, When It Doesn't
Use this server if your primary need is multi-variable risk quantification or facility-wide oversight. If you must know the spoilage risk (combining temp + humidity + grain type), get_spoilage_risk is your main tool. If you are only checking a single, immediate data point—say, just the current temperature—you might be better off using a dedicated sensor dashboard built directly from the API. Don't rely on this server if you only need to list silo names; use get_silos. But if you need to know what those silos are monitoring and how they relate to each other, Grain Watch is the tool.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Grain Watch. 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.
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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 12 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
The nightmare of manual inspections
Right now, checking a facility's status means jumping between separate dashboards: one for temperature, another for humidity, and then cross-referencing those against inventory sheets. You manually check the 'Critical Alerts' tab, copy temperatures into Excel, and then wait for an engineer to correlate that raw data with the grain type stored.
With Grain Watch MCP, you ask your agent a single question—like 'What is the spoilage risk in Silo 5?' The agent runs `get_silo_details`, pulls live readings from `get_current_temperature` and `get_current_humidity`, calculates the score using `get_spoilage_risk`, and gives you one final, actionable number. It's done.
Grain Watch MCP Server: Get a full spoilage risk assessment
Before this server, determining if grain was at risk required multiple steps: checking the temperature trend, verifying the humidity level against optimal storage ranges, and then manually cross-referencing that with the specific grain type. It was slow, and you often missed subtle developing issues.
Now, the agent handles all of that complexity automatically. You simply ask for the spoilage risk, and it synthesizes data from temperature, humidity, and grain metadata into a single predictive score. No more guesswork.
Common Questions About Grain Watch MCP
How do I check if any hot spots are developing in my facility? +
get_hotspot_alerts checks for localized heating, which is an early warning sign of spoilage. It returns the severity and location, helping you act before the problem gets worse.
What if I only want to know about one silo's temperature? +
get_current_temperature lets you target a single silo for immediate readings from all sensor zones (top, middle, bottom). This is faster than running the full facility overview.
How do I check if my sensors are reliable before trusting the data? +
Run get_sensor_health. This tool confirms if the sensor IDs are active, what their battery levels are, and if they have gone offline. Always verify sensor health first.
Can I track how humidity changes over time using get_humidity_history? +
Yes, get_humidity_history pulls time-series data for you. This is essential for seeing moisture migration patterns and knowing if condensation risk was present last week.
How do I use `get_silos` to get a list of all monitored storage units? +
You run get_silos to pull an inventory of every silo you monitor. It returns the IDs, names, grain types, and current monitoring status for your entire facility. This is the best place to start when you need an overall list before running detailed temperature or alert checks.
What does `get_spoilage_risk` calculate regarding my stored grain? +
The tool gives you a calculated risk level (low, moderate, high, critical). It combines current temperature, humidity, and grain type data to predict potential spoilage dates. The output also recommends specific actions you can take immediately.
How do I check the physical placement of my sensors using `get_sensor_map`? +
get_sensor_map lists every sensor ID and its exact location within the silo. It provides details like top/middle/bottom depth and zone position. This helps you understand where a reading came from, which is key when analyzing temperature gradients.
What kind of issues are covered by `get_alerts`? +
get_alerts collects all active warnings for the facility. It tracks critical and warning levels across three areas: temperature changes, sudden humidity shifts, and sensor system failures. Each alert includes a recommended action.
Can my AI detect hot spots developing in my grain silos before spoilage occurs? +
Yes! Use the get_hotspot_alerts tool to check for active hot spot detections across your silos. Hot spots are localized temperature increases that indicate early biological activity (mold, insects, or grain respiration) before visible spoilage. For trend analysis, use get_temperature_history to see how temperatures have been changing over the past days or weeks. Early hot spot detection gives you critical time to activate aeration and prevent grain loss.
How do I get the AI spoilage risk assessment for my silos? +
Use the get_spoilage_risk tool with your silo ID. Grain Watch AI analyzes temperature trends, humidity patterns, and grain type to provide a risk level (low, moderate, high, critical), contributing factors, predicted days until spoilage if conditions persist, and recommended preventive actions. This combines multiple data sources into a single actionable assessment. For a facility-wide view, use get_facility_overview to see overall risk across all silos.
Can I check the health of my temperature sensors to ensure reliable monitoring? +
Yes! Use the get_sensor_health tool with your silo ID to check the status of all temperature and humidity sensors. This shows which sensors are active, offline, or faulted, along with last communication times and battery levels for wireless sensors. You can also use get_sensor_map to see the physical layout of all sensors in a silo, helping you understand which zones each sensor monitors.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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