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Use Centaur Analytics with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Access AI-powered grain monitoring via Centaur. track CO2, moisture, temperature, predict spoilage, and forecast grain quality from any AI agent.

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

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Complete set · 12 capabilities

The complete Centaur Analytics capability set.

These are the exact actions your AI can choose when you ask it to work with Centaur Analytics.

Capability set01 / 03

01-04

4 capabilities in this set.

Part of 12 available through Centaur Analytics.

  1. 01

    Get co2 history

    CO2 is the earliest indicator of biological activity (mold, insects, grain respiration) that leads to spoilage. Returns time-series CO2 data in ppm with timestamps. Essential for spoilage trend analysis, early warning detection, and validating storage condition stability. AI agents should reference this when users ask "show me CO2 trends for bin 3 over the past 30 days", "has CO2 been rising in silo 5", or need historical CO2 data for grain quality assessment. Optional days parameter controls lookback period. Get historical CO2 readings to track spoilage trends over time

  2. 02

    Get current readings

    Returns CO2 levels (ppm), moisture content (%), and temperature (C) from multiple sensor positions throughout the grain mass. Essential for real-time grain quality monitoring, early spoilage detection, and storage condition assessment. AI agents should use this when users ask "what are the current conditions in bin 2", "show me all sensor readings for silo 4", or need immediate grain quality data for storage management decisions. Get current CO2, moisture, and temperature readings from all sensors in a bin

  3. 03

    Get quality report

    Combines current sensor readings, historical trends, spoilage predictions, quality forecasts, and actionable recommendations into a single report. Includes test weight estimates, moisture stability analysis, temperature uniformity assessment, and mycotoxin risk evaluation. Essential for quality documentation, marketing decisions, insurance claims, and comprehensive grain condition assessment. AI agents should reference this when users ask "generate a quality report for bin 2", "give me the complete grain condition assessment for silo 4", or need comprehensive quality documentation for a specific bin. Get a comprehensive AI-generated quality report for a specific grain bin

  4. 04

    Get spoilage predictions

    Returns spoilage risk level (low, moderate, high, critical), predicted days until spoilage onset, confidence scores, and recommended preventive actions. Essential for proactive grain management, early intervention planning, and quality preservation. AI agents should use this when users ask "what is the spoilage risk for bin 3", "when will grain quality degrade in silo 5", or need AI-driven risk assessments for storage management decisions. Get AI-powered spoilage risk predictions for a specific grain bin

Capability set02 / 03

05-08

4 capabilities in this set.

Part of 12 available through Centaur Analytics.

  1. 05

    Get temperature history

    Temperature increases often indicate active spoilage, insect activity, or mold growth. Returns time-series temperature data (Celsius) with timestamps from multiple sensor depths and positions. Essential for hot spot detection, spoilage heating identification, aeration effectiveness evaluation, and grain quality preservation. AI agents should reference this when users ask "show me temperature trends for bin 4", "are there any hot spots developing in silo 6", or need historical temperature data for spoilage analysis. Get historical temperature readings to detect hot spots and spoilage heating

  2. 06

    Get alerts

    Alerts are triggered by threshold breaches (high CO2, rising temperature, moisture migration, sensor failures) and indicate conditions requiring immediate attention. Returns alert severity (critical, warning, info), alert type, affected bin, timestamp, and recommended actions. Essential for proactive grain management, quality issue detection, and operational response. AI agents should use this when users ask "show me all active alerts", "what warnings have been triggered for bin 3", or need alert data for operational monitoring. Optional bin_id filters alerts for a specific bin. Get active alerts and warnings for grain bins or a specific bin

  3. 07

    Get bin details

    Essential for understanding bin context before analyzing sensor data, planning aeration strategies, or generating quality reports. AI agents should reference this when users ask "tell me about bin 5", "what grain is stored in silo 3", or need detailed bin metadata for informed analysis. Get detailed information about a specific grain storage bin

  4. 08

    Get bins

    Returns bin IDs, names, locations, grain types, fill levels, and current monitoring status. Essential for facility overview, bin inventory management, and selecting specific bins for detailed analysis. AI agents should use this when users ask "show me all my grain bins", "list monitored storage units", or need to identify available bins before querying sensor readings or AI predictions. List all grain storage bins monitored by Centaur Analytics

Capability set03 / 03

09-12

4 capabilities in this set.

Part of 12 available through Centaur Analytics.

  1. 09

    Get facility overview

    Essential for executive reporting, facility-wide quality assessment, and strategic storage management. AI agents should use this when users ask "give me an overview of my entire facility", "what is the overall grain quality status", or need facility-level summaries for management reporting. Get comprehensive overview of the entire grain storage facility

  2. 10

    Get moisture history

    Moisture migration and condensation are key drivers of spoilage and quality loss. Returns time-series moisture data (%) with timestamps from multiple sensor positions. Essential for moisture migration analysis, condensation detection, drying effectiveness assessment, and storage safety monitoring. AI agents should use this when users ask "show me moisture trends for bin 1", "has moisture been stable in silo 2", or need historical moisture data for storage management. Get historical moisture content readings for grain storage analysis

  3. 11

    Get quality forecast

    Uses computer simulation models combining current sensor data, weather forecasts, and grain characteristics. Essential for marketing timing, quality preservation planning, and storage duration optimization. AI agents should reference this when users ask "what will the grain quality be in bin 2 next month", "forecast quality changes for silo 4", or need predictive quality data for marketing and storage decisions. Get AI-powered grain quality forecast for upcoming weeks

  4. 12

    Get sensor health

    Returns sensor IDs, positions (depth/location), battery levels, signal strength, last communication time, and operational status (active, low battery, offline). Essential for sensor network maintenance, data continuity assurance, and monitoring system reliability. AI agents should reference this when users ask "are all sensors working in bin 5", "which sensors need battery replacement", or need sensor network health data for system administration. Get health status and battery levels of wireless sensors in a grain bin

Observed, not estimated

901ms average. Fast in production.

Centaur Analytics is checked daily against the live service.

Daily averagePeak 1051ms
Aug 20Today
Fastest day
756ms
Slowest day
1051ms
14-day trend
Slowing+12%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 12 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Centaur Analytics, so you can see the experience inside your AI.

It does not authenticate your account with Centaur Analytics. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Centaur Analytics Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_JAUlTOok6viAadhbZwv9ZjIVKtoBneQMgOzJf2Ty/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Centaur Analytics capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "centaur-analytics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_JAUlTOok6viAadhbZwv9ZjIVKtoBneQMgOzJf2Ty/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

FAQ

Questions Centaur Analytics owners ask.

  • 01

    Can my AI predict when grain spoilage will start in my storage bin?

    Yes! Use the get_spoilage_predictions capability with your bin ID. Centaur AI analyzes CO2 trends, moisture patterns, and temperature data to predict spoilage risk (low, moderate, high, critical) and estimated days until spoilage onset. For deeper analysis, combine with get_co2_history to see the CO2 trend that drives the prediction. CO2 is the earliest spoilage indicator, often rising days before temperature changes become apparent.

  • 02

    How do I monitor CO2 levels to detect early signs of grain spoilage?

    Use get_current_readings for real-time CO2 levels across all sensor positions in a bin, then use get_co2_history with a 30-day lookback to identify trends. CO2 levels above 1500 ppm indicate biological activity, and rising trends signal developing spoilage. Set up get_alerts to receive automatic warnings when CO2 exceeds safe thresholds. Early CO2 detection gives you 7-14 days more lead time than temperature-based monitoring alone.

  • 03

    Can I get an AI-generated quality report for a specific bin to share with buyers?

    Yes! Use the get_quality_report capability with your bin ID to generate a comprehensive AI-powered quality report. This combines current sensor readings, historical trends, spoilage predictions, and quality forecasts into a single professional report including test weight estimates, moisture stability analysis, temperature uniformity, and mycotoxin risk evaluation. Perfect for buyer communications, insurance documentation, and quality certification.

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