# Wiagro MCP

> 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.

## Overview
- **Category:** iot-hardware
- **Price:** Free
- **Tags:** grain-monitoring, iot-sensors, environmental-tracking, silo-management, quality-control, remote-sensing

## Description

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.

## Tools

### 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.

### get_current_readings
Provides an immediate snapshot of temperature, intergranular humidity percentage, and current CO2 level from all active sensors in a silo bag.

### get_facility_overview
Generates a high-level summary report covering the overall quality status and condition of every monitored silobag across the entire site.

### 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 conditions around the storage facility.

### get_sensor_health
Checks the operational status of every IoT sensor, reporting on battery life, signal strength, and last communication time to ensure data reliability.

### get_silobag_details
Provides specific metadata about a single silobag, including its type, grain contents, and overall structural information.

### get_silobags
Lists every monitored silo bag or conventional storage unit, giving names, locations, fill levels, and current monitoring status.

### get_temperature_history
Graphs historical temperature readings over a chosen period to identify hot spots or sustained warming trends that signal spoilage heating.

## Prompt Examples

**Prompt:** 
```
Show me the current temperature, humidity, and CO2 readings for silobag 3.
```

**Response:** 
```
Fetching current readings for silobag 3 (Corn, 85% full)... Temperature: 22.3°C average across all sensors (max 23.1°C at center, min 21.5°C at edges). Humidity: 14.8% intergranular humidity (stable, within safe range). CO2: 920 ppm (normal, no biological activity detected). All 12 IoT sensors reporting healthy. Assessment: Grain condition is GOOD — no spoilage indicators detected. Temperature is uniform with no hot spots. Continue routine monitoring. Would you like to see the 30-day temperature trend?
```

**Prompt:** 
```
Check for any silobag rupture alerts or active warnings across my facility.
```

**Response:** 
```
Checking facility alerts... No rupture alerts detected — all silobags are structurally intact according to satellite monitoring. Active warnings: 1 alert found. Silobag 7 — CO2 level approaching threshold (1450 ppm, warning level is 1500 ppm). Recommended action: Increase monitoring frequency and consider targeted aeration if CO2 continues rising. All other silobags are within normal parameters. Facility quality score: 94/100. Would you like detailed sensor readings for silobag 7?
```

**Prompt:** 
```
Give me a quality assessment for all my monitored silobags.
```

**Response:** 
```
Generating quality assessments... Silobag 1 (Soybeans): Quality score 91/100 — Excellent condition, stable temperature at 18°C, CO2 at 780 ppm. Estimated storage life: 4+ months. Silobag 2 (Wheat): Quality score 87/100 — Good condition, minor temperature variation (20-22°C), CO2 at 890 ppm. Storage life: 3+ months. Silobag 3 (Corn): Quality score 94/100 — Excellent condition, uniform temperature, CO2 at 920 ppm. Storage life: 4+ months. Silobag 7 (Sorghum): Quality score 72/100 — FAIR condition, elevated CO2 trend (1450 ppm), requires closer monitoring. Storage life: 1-2 months if conditions stabilize. Priority action: Investigate silobag 7 CO2 increase. Overall facility average: 86/100.
```

## Capabilities

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## Use Cases

### 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.

## Benefits

- 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.

## How It Works

The bottom line is you get one conversational interface that handles complex industrial diagnostics across dozens of sensors and reporting systems.

1. Subscribe to this MCP and enter your Wiagro API key and base URL into your Vinkius client connection.
2. Ask your AI agent for a facility overview or current readings, letting it run the diagnostic checks automatically.
3. The agent processes all live data—from sensor health reports to historical CO2 trends—and delivers a single, actionable summary.

## Frequently Asked Questions

**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.