Centaur Analytics MCP Server for Windsurf 12 tools — connect in under 2 minutes
Windsurf brings agentic AI coding to a purpose-built IDE. Connect Centaur Analytics through Vinkius and Cascade will auto-discover every tool. ask questions, generate code, and act on live data without leaving your editor.
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Vinkius Desktop App
The modern way to manage MCP Servers — no config files, no terminal commands. Install Centaur Analytics and 2,500+ MCP Servers from a single visual interface.
{
"mcpServers": {
"centaur-analytics": {
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}
}
}
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Centaur Analytics MCP Server
Connect your Centaur Analytics Internet-of-Crops API to any AI agent and take full control of AI-powered grain quality monitoring, predictive spoilage detection, wireless sensor management, and enterprise grain storage intelligence through natural conversation.
Windsurf's Cascade agent chains multiple Centaur Analytics tool calls autonomously. query data, analyze results, and generate code in a single agentic session. Paste Vinkius Edge URL, reload, and all 12 tools are immediately available. Real-time tool feedback appears inline, so you see API responses directly in your editor.
What you can do
- Bin Management — List and manage all grain storage bins with fill levels, grain types, and monitoring status
- Real-Time Readings — Get current CO2, moisture, and temperature readings from wireless sensors throughout the grain mass
- CO2 Tracking — Monitor historical CO2 trends as the earliest indicator of biological activity and spoilage
- Moisture Analysis — Track moisture content and migration patterns to detect condensation and quality risks
- Temperature Monitoring — Detect hot spots and spoilage heating with distributed temperature sensor data
- AI Spoilage Predictions — Receive machine learning-powered spoilage risk assessments with days-to-spoilage estimates
- Quality Forecasting — Predict future grain quality metrics using computer simulation models
- Alert Management — Monitor active alerts for high CO2, rising temperature, moisture issues, and sensor failures
- Sensor Health — Track wireless sensor battery levels, signal strength, and operational status
- Facility Overview — Get comprehensive facility-wide summaries for executive reporting and strategic management
- Quality Reports — Generate AI-powered comprehensive quality reports with actionable recommendations
The Centaur Analytics MCP Server exposes 12 tools through the Vinkius. Connect it to Windsurf in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Centaur Analytics to Windsurf via MCP
Follow these steps to integrate the Centaur Analytics MCP Server with Windsurf.
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
Add the server
Paste the JSON configuration above into mcp_config.json
Save and reload
Windsurf will detect the new server automatically
Start using Centaur Analytics
Open Cascade and ask: "Using Centaur Analytics, help me...". 12 tools available
Why Use Windsurf with the Centaur Analytics MCP Server
Windsurf provides unique advantages when paired with Centaur Analytics through the Model Context Protocol.
Windsurf's Cascade agent autonomously chains multiple tool calls in sequence, solving complex multi-step tasks without manual intervention
Purpose-built for agentic workflows. Cascade understands context across your entire codebase and integrates MCP tools natively
JSON-based configuration means zero code changes: paste a URL, reload, and all 12 tools are immediately available
Real-time tool feedback is displayed inline, so you see API responses directly in your editor without switching contexts
Centaur Analytics + Windsurf Use Cases
Practical scenarios where Windsurf combined with the Centaur Analytics MCP Server delivers measurable value.
Automated code generation: ask Cascade to fetch data from Centaur Analytics and generate models, types, or handlers based on real API responses
Live debugging: query Centaur Analytics tools mid-session to inspect production data while debugging without leaving the editor
Documentation generation: pull schema information from Centaur Analytics and have Cascade generate comprehensive API docs automatically
Rapid prototyping: combine Centaur Analytics data with Cascade's code generation to scaffold entire features in minutes
Centaur Analytics MCP Tools for Windsurf (12)
These 12 tools become available when you connect Centaur Analytics to Windsurf via MCP:
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
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
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
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
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
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
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
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
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
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
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
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
Example Prompts for Centaur Analytics in Windsurf
Ready-to-use prompts you can give your Windsurf agent to start working with Centaur Analytics immediately.
"Show me the current CO2, moisture, and temperature readings for bin 5."
"What is the AI spoilage prediction for my soybean bin?"
"Give me a facility-wide overview of all grain bins and any active alerts."
Troubleshooting Centaur Analytics MCP Server with Windsurf
Common issues when connecting Centaur Analytics to Windsurf through the Vinkius, and how to resolve them.
Server not connecting
Centaur Analytics + Windsurf FAQ
Common questions about integrating Centaur Analytics MCP Server with Windsurf.
How does Windsurf discover MCP tools?
mcp_config.json file on startup and connects to each configured server via Streamable HTTP. Tools are listed in the MCP panel and available to Cascade automatically.Can Cascade chain multiple MCP tool calls?
Does Windsurf support multiple MCP servers?
mcp_config.json. Each server's tools appear in the MCP panel and Cascade can use tools from different servers in a single flow.Connect Centaur Analytics with your favorite client
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Connect Centaur Analytics to Windsurf
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.