Wiagro MCP for AI. Stop reacting to problems. Start predicting them.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Wiagro connects any AI client to full-spectrum grain monitoring data, giving you real-time control over stored commodities. It tracks temperature, humidity, CO2 levels, and structural integrity of silobags from anywhere.
Need to know if spoilage is starting or if a bag has a tear? This MCP gives your agent the full picture, predicting quality risks before they become visible.
What your AI can do
Get rupture alerts
Identifies structural damage, like tears or holes in the silobags, providing the location of the rupture and its severity level.
Get alerts
Retrieves all active warnings, detailing the severity, type, affected silobag, and recommended action for immediate attention.
Get co2 history
Pulls time-series data showing CO2 levels in parts per million (ppm), which indicates if biological activity is starting or escalating inside the grain mass.
Ask an AI about this
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Wiagro: 12 Monitoring Tools
These tools allow you to query every aspect of the silo network—from historical trends to real-time sensor status—all in one place.
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 Wiagro on VinkiusGet Rupture Alerts
Identifies structural damage, like tears or holes in the silobags, providing the location of the rupture and its severity level.
Get Alerts
Retrieves all active warnings, detailing the severity, type, affected silobag, and...
Get Co2 History
Pulls time-series data showing CO2 levels in parts per million (ppm), which...
Get Current Readings
Provides an immediate snapshot of temperature, intergranular humidity percentage...
Get Facility Overview
Generates a high-level summary report covering the overall quality status and...
Get Humidity History
Tracks historical humidity patterns over time, helping you detect moisture migration or condensation risks within the stored grain.
Get Quality Assessment
Runs an AI-powered analysis to give a quality score and estimate remaining storage life for any specific silobag.
Get Satellite Data
Accesses external environmental data from satellite sources that could affect...
Get Sensor Health
Checks the operational status of every IoT sensor, reporting on battery life, signal...
Get Silobag Details
Provides specific metadata about a single silobag, including its type, grain...
Get Silobags
Lists every monitored silo bag or conventional storage unit, giving names...
Get Temperature History
Graphs historical temperature readings over a chosen period to identify hot spots or sustained warming trends that signal spoilage heating.
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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 Wiagro, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
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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 connection provides 12 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Handling Silo Diagnostics Manually is a Nightmare
Today, checking your grain storage requires logging into separate dashboards for temperature logs, CO2 readings, and structural alerts. You spend minutes copying data from one tab to another in an Excel sheet just to create a summary report that management actually reads.
With this MCP, you stop clicking through tabs. Your agent pulls all the required historical trends, real-time sensor status, and quality scores automatically. It gives you one single narrative: 'The corn silo is good, but watch out for moisture buildup in corner three.'
Get Silobag Details with `get_silobag_details`
Before running any diagnostic check on a specific unit, you currently have to manually confirm the silo bag's contents (soybeans or wheat) and its exact location. This is critical because different grains react differently to environmental stress.
Now, use `get_silobag_details` first. It gives you the necessary context—the grain type and specific metadata—so your AI agent knows exactly what it's advising on. The advice is immediately more accurate.
What your AI can actually do with this
Managing large storage facilities means constantly worrying about what's happening inside those silos. Grain degradation doesn't wait for an inspection team; it happens slowly through temperature spikes, moisture migration, or CO2 buildup. This MCP connects your AI agent directly to the entire IoT network, making you a proactive grain preservation analyst.
You can get immediate readings of temp, humidity, and CO2 from multiple sensor points across every silobag. Want to see if spoilage is trending? Check historical data for CO2 spikes or temperature fluctuations over months. Worried about structural failure? The system tracks satellite alerts for tears or holes immediately. Because this process involves so much critical infrastructure data—from real-time readings to predictive quality scores—you need total visibility.
That's where Vinkius AI Analytics comes in. It gives you a full audit trail of every tool call and every piece of data that moves through the system, ensuring nothing happens in the dark when your agent is running complex diagnostics.
It cuts out the manual work entirely. Instead of logging into five different dashboards to check sensor health, facility status, and quality scores, you simply ask your AI client for an overview, and it runs all the necessary checks automatically.
019d7622-7f6d-7233-8058-898a4fed830f Here's how it actually works
The bottom line is you get one conversational interface that handles complex industrial diagnostics across dozens of sensors and reporting systems.
Subscribe to this MCP and enter your Wiagro API key and base URL into your Vinkius client connection.
Ask your AI agent for a facility overview or current readings, letting it run the diagnostic checks automatically.
The agent processes all live data—from sensor health reports to historical CO2 trends—and delivers a single, actionable summary.
Who is this actually for?
Grain farmers who hate waiting for physical inspections. Facility managers tired of juggling multiple IoT dashboards. Agricultural consultants needing instant, data-driven reports to advise clients on storage risk.
Uses this MCP to run a facility overview and check get_sensor_health status across hundreds of sensors before routine maintenance begins.
Checks for any active warnings using get_alerts immediately after a weather event or during an unexpected temperature spike.
Runs get_quality_assessment on multiple clients' silos to provide comparative market advice based on spoilage risk and storage life.
What Changes When You Connect
Don't just check current readings; use get_current_readings combined with get_alerts to get a single, immediate report on any critical condition (temp, humidity, CO2).
Avoid manual spreadsheet logging. Running the get_facility_overview provides an instant summary of every unit's status for executive reports.
Before doing any maintenance, always check get_sensor_health. This ensures that low data quality isn't mistaken for actual spoilage issues.
When planning storage duration, let the AI run get_quality_assessment to get a reliable score and estimated remaining life, informing marketing decisions.
If you suspect contamination, compare historical CO2 spikes from get_co2_history against temperature trends found in get_temperature_history for root cause analysis.
See it in action
Detecting a hidden spoilage risk
The farmer suspects something is wrong but doesn't know where. They ask their agent to assess the facility, and the MCP runs get_facility_overview, identifies an elevated CO2 trend using get_co2_history in Silobag 7, and flags it for immediate investigation.
Responding to structural damage
A manager receives a weather warning. They ask their agent to check the facility integrity, which triggers get_rupture_alerts. The agent immediately reports that Silobag 3 has experienced a tear, preventing potential crop loss.
Assessing overall site readiness
The consultant needs to pitch their services. They run the MCP against all sites, getting get_silobags first, then running get_sensor_health on each one. This confirms that 8/10 units are fully operational before presenting a final report.
Pinpointing moisture movement
The engineer notices uneven quality scores. They ask the agent to compare get_humidity_history against get_temperature_history. The MCP pinpoints that condensation is happening in the upper section of Silobag 5 because humidity peaked right after a temperature drop.
The honest tradeoffs
Checking data point by data point
The user runs get_current_readings for Silo A, then has to run get_alerts separately for Silo B, and finally gets a separate report from get_quality_assessment.
Ask your agent for an 'Overall Facility Status Report.' It automatically combines the results of get_facility_overview, get_current_readings, and get_alerts into one conversational answer.
Ignoring physical context
The user sees a high CO2 spike from get_co2_history but doesn't know which silo bag it belongs to or what grain type is stored.
First, run get_silobags to list all available units. Then specify the ID in your request so the agent can accurately cross-reference the CO2 data with the correct storage unit.
Overlooking system reliability
The AI gives a great quality score, but the user never checks if the sensors are actually functional.
Always run get_sensor_health first. If any sensor is reporting low battery or offline status, you know the data, even if it looks normal, might be unreliable.
When It Fits, When It Doesn't
Use this MCP when your primary need is continuous, predictive monitoring of physical conditions (temp, humidity, CO2) and structural integrity. If you are only checking inventory lists or basic contact information, don't use it; simply list the silobags first. However, if you only need to see a single reading without context, running get_current_readings is fine, but remember that this tool doesn't tell you why the readings are high—it just shows them. For true diagnosis and actionable advice, always use the agent to chain multiple tools together (like combining get_co2_history with get_temperature_history) to build a full picture.
Questions you might have
How do I check if there are any active warnings using get_alerts? +
The get_alerts tool retrieves all immediate warnings, telling you the severity (critical or warning) and exactly which silobag is affected. It's your first step when you need an urgent status update.
What does get_co2_history tell me about my grain? +
get_co2_history tracks how CO2 levels change over time, which is the earliest sign of biological activity. If the readings trend up, it means spoilage or mold growth is starting.
Can I check sensor reliability using get_sensor_health? +
Yes. get_sensor_health reports on every sensor's battery life and signal strength. This ensures that if you are getting bad data, you know whether it’s a system problem or an actual grain issue.
How do I assess the overall facility condition? +
Run get_facility_overview. This tool compiles all the necessary information—from the current status of every silo to the average quality score—into a single, executive-ready summary.
What is the difference between get_temperature_history and get_co2_history? +
get_temperature_history shows heat spikes that can cause spoilage. get_co2_history tracks biological activity (like mold or insects) which often happens before you see a temperature change.
How do I list all monitored storage units before running a detailed check using get_silobags? +
It returns an inventory of every silobag or conventional silo you track. This is essential for facility overview and helps you confirm the IDs and grain types available before querying sensor data or alerts.
If I suspect physical damage, how do I use get_rupture_alerts? +
This tool provides immediate warnings about tears, holes, or structural breaches in your silobags. It’s critical for protecting the stored grain from external contaminants and weather exposure.
What context do I need before running advanced checks using get_silobag_details? +
The tool gives you detailed metadata, like the specific grain type or physical location of a silobag. Knowing this context ensures that all subsequent readings are analyzed accurately for the correct crop.
Can my AI detect if a silobag has been ruptured or damaged? +
Yes! Use the get_rupture_alerts tool to check for satellite-detected silobag ruptures, tears, or structural damage. Wiagro uses satellite imagery analysis to identify breaches in silobag integrity that could expose grain to weather and pests. For a complete picture, combine with get_alerts to see temperature, humidity, and CO2 alerts that may indicate secondary effects of a rupture.
How do I monitor CO2 levels to detect early grain spoilage in silobags? +
Use the get_co2_history tool with your silobag ID and a date range (e.g., 30 days) to see CO2 trends over time. Rising CO2 levels indicate biological activity from mold, insects, or grain respiration — often appearing before temperature changes. Combine with get_current_readings for real-time CO2 status and get_alerts to check for any active CO2 warnings.
Can I check the health status of sensors in my silobag monitoring system? +
Yes! Use the get_sensor_health tool with your silobag ID to check battery levels, signal strength, and operational status of all IoT sensors. This helps you identify sensors that need battery replacement or have gone offline, ensuring continuous monitoring coverage. For a facility-wide view, use get_facility_overview to see the overall health of your monitoring system.
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