OpenWeather Agro MCP Server for Cursor 12 tools — connect in under 2 minutes
Cursor is an AI-first code editor built on VS Code that integrates LLM-powered coding assistance directly into the development workflow. Its Agent mode enables autonomous multi-step coding tasks, and MCP support lets agents access external data sources and APIs during code generation.
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{
"mcpServers": {
"openweather-agro": {
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}
}
}
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About OpenWeather Agro MCP Server
Connect your OpenWeather Agro API to any AI agent and take full control of satellite-based vegetation monitoring, weather-driven agricultural insights, and precision farming data through natural conversation.
Cursor's Agent mode turns OpenWeather Agro into an in-editor superpower. Ask Cursor to generate code using live data from OpenWeather Agro and it fetches, processes, and writes. all in a single agentic loop. 12 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.
What you can do
- NDVI Analysis — Monitor crop vegetation health with satellite-derived NDVI values
- EVI Monitoring — Track enhanced vegetation index for high-biomass and dense canopy areas
- Soil Temperature — Check soil thermal conditions for seed germination and root activity
- Evapotranspiration — Calculate crop water use for precision irrigation scheduling
- Current Weather — Get real-time weather conditions for daily farming decisions
- Weather Forecast — Access 5-day forecasts for planting and harvest planning
- Historical Weather — Retrieve past weather data for crop performance analysis
- Growing Degree Days — Track heat accumulation for crop development staging
- Satellite Imagery — Access satellite imagery for visual field assessment
- Historical NDVI — Analyze vegetation health trends over growing seasons
- Crop Health Index — Get comprehensive crop condition scores
- Frost Risk — Assess frost danger for crop protection planning
The OpenWeather Agro MCP Server exposes 12 tools through the Vinkius. Connect it to Cursor 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 OpenWeather Agro to Cursor via MCP
Follow these steps to integrate the OpenWeather Agro MCP Server with Cursor.
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
Save the file
Cursor will automatically detect the new MCP server
Start using OpenWeather Agro
Open Agent mode in chat and ask: "Using OpenWeather Agro, help me...". 12 tools available
Why Use Cursor with the OpenWeather Agro MCP Server
Cursor AI Code Editor provides unique advantages when paired with OpenWeather Agro through the Model Context Protocol.
Agent mode turns Cursor into an autonomous coding assistant that can read files, run commands, and call MCP tools without switching context
Cursor's Composer feature can generate entire files using real-time data fetched through MCP. no copy-pasting from external dashboards
MCP tools appear alongside built-in tools like file reading and terminal access, creating a unified agentic environment
VS Code extension compatibility means your existing workflow, keybindings, and extensions all work alongside MCP tools
OpenWeather Agro + Cursor Use Cases
Practical scenarios where Cursor combined with the OpenWeather Agro MCP Server delivers measurable value.
Code generation with live data: ask Cursor to generate a security report module using live DNS and subdomain data fetched through MCP
Automated documentation: have Cursor query your API's tool schemas and generate TypeScript interfaces or OpenAPI specs automatically
Infrastructure-as-code: Cursor can fetch domain configurations and generate corresponding Terraform or CloudFormation templates
Test scaffolding: ask Cursor to pull real API responses via MCP and generate unit test fixtures from actual data
OpenWeather Agro MCP Tools for Cursor (12)
These 12 tools become available when you connect OpenWeather Agro to Cursor via MCP:
get_crop_health_index
CHI provides a single metric for overall crop health, making it easier to track field conditions over time and compare across fields. Essential for quick field health assessment, prioritizing scouting missions, and communicating crop status to stakeholders. AI agents should use this when users ask "what is the overall crop health score for my field", "get a quick health assessment", or need a simplified crop condition metric. Date format: YYYY-MM-DD. Get Crop Health Index (CHI) for comprehensive crop condition assessment
get_current_weather
Essential for daily farming decisions, spray application timing, harvest planning, and frost protection. AI agents should use this when users ask "what is the weather like at my farm right now", "should I spray pesticides today", or need current weather data for agricultural operations. Get current weather conditions for agricultural decision making
get_evapotranspiration
ET combines soil evaporation and plant transpiration, providing the most accurate measure of crop water use. Essential for precision irrigation scheduling, water resource management, and drought assessment. AI agents should reference this when users ask "what is the evapotranspiration rate for my field", "calculate irrigation needs", or need crop water use data for irrigation planning. Date format: YYYY-MM-DD. Get evapotranspiration rates for irrigation scheduling and water management
get_evi
EVI is more sensitive than NDVI in high-biomass regions and less affected by atmospheric conditions and soil background. Essential for monitoring dense canopies, tropical crops, and areas with high vegetation cover. AI agents should reference this when users ask "what is the EVI for my dense crop area", "monitor high-biomass vegetation", or need enhanced vegetation index for areas where NDVI saturates. Date format: YYYY-MM-DD. Get EVI (Enhanced Vegetation Index) for high-biomass crop monitoring
get_frost_risk
Returns risk levels (low, moderate, high, critical), predicted frost timing, and recommended protection measures. Essential for frost-sensitive crops (fruits, vegetables, vineyards), irrigation-based frost protection, and crop insurance documentation. AI agents should reference this when users ask "is there frost risk for my orchard tonight", "assess frost danger for my crops", or need frost warning data for crop protection planning. Get frost risk assessment for crop protection planning
get_growing_degree_days
GDD measures heat accumulation used to predict crop development stages, pest emergence, and harvest timing. Essential for phenology tracking, variety selection, and timing agricultural operations. AI agents should reference this when users ask "calculate GDD for my corn field this season", "track crop development stages", or need heat unit accumulation data for agricultural planning. Date format: YYYY-MM-DD. Calculate Growing Degree Days (GDD) for crop development tracking
get_historical_ndvi
Returns time-series NDVI values showing vegetation health progression, stress detection, and recovery patterns. Essential for seasonal crop performance comparison, drought impact assessment, and long-term field health monitoring. AI agents should reference this when users ask "show me NDVI trends for my field over the growing season", "compare vegetation health between seasons", or need historical vegetation index data for agricultural trend analysis. Date format: YYYY-MM-DD. Get historical NDVI trends for seasonal vegetation health analysis
get_ndvi
NDVI ranges from -1 to 1, with higher values (0.6-0.9) indicating healthy dense vegetation and lower values (0.2-0.5) indicating stressed or sparse vegetation. Essential for crop health monitoring, growth stage assessment, and yield prediction. AI agents should use this when users ask "what is the NDVI for my field on this date", "check crop vegetation health", or need satellite-based vegetation index data for agricultural analysis. Date format: YYYY-MM-DD. Get NDVI (Normalized Difference Vegetation Index) for crop health assessment
get_satellite_imagery
Returns imagery metadata and access URLs for visual crop assessment, field boundary verification, and change detection analysis. Essential for remote field monitoring, damage assessment, and visual crop health evaluation. AI agents should use this when users ask "get satellite imagery for my field", "show me the latest satellite view of my farm", or need visual imagery for agricultural monitoring. Date format: YYYY-MM-DD. Zoom: 1-16. Get satellite imagery for visual crop assessment and field monitoring
get_soil_temperature
Soil temperature is critical for seed germination timing, root activity assessment, and nutrient uptake optimization. Essential for planting decisions, irrigation scheduling, and soil health monitoring. AI agents should use this when users ask "what is the soil temperature for planting", "check if soil is warm enough for germination", or need soil thermal data for agricultural planning. Date format: YYYY-MM-DD. Get satellite-derived soil temperature for seed germination and root activity assessment
get_weather_forecast
Essential for planting schedules, harvest timing, spray application windows, and irrigation planning. AI agents should reference this when users ask "what is the weather forecast for my farm this week", "will it rain in the next 5 days", or need forward-looking weather data for agricultural planning. Get multi-day weather forecast for agricultural planning
get_weather_history
Essential for comparing current conditions with historical patterns, analyzing crop performance under past weather conditions, and validating crop models. AI agents should use this when users ask "what was the weather like on this date last year", "show me historical weather for my field", or need past weather data for agricultural analysis. Date format: Unix timestamp (seconds since 1970). Get historical weather data for crop analysis and trend assessment
Example Prompts for OpenWeather Agro in Cursor
Ready-to-use prompts you can give your Cursor agent to start working with OpenWeather Agro immediately.
"What is the NDVI for my corn field at coordinates 41.8780, -93.0977 on April 1st?"
"Calculate the growing degree days for my wheat field from March 1 to today."
"Is there frost risk for my vineyard tonight? I need to know if I should turn on the wind machines."
Troubleshooting OpenWeather Agro MCP Server with Cursor
Common issues when connecting OpenWeather Agro to Cursor through the Vinkius, and how to resolve them.
Tools not appearing in Cursor
Server shows as disconnected
OpenWeather Agro + Cursor FAQ
Common questions about integrating OpenWeather Agro MCP Server with Cursor.
What is Agent mode and why does it matter for MCP?
Where does Cursor store MCP configuration?
mcp.json file. You can configure servers at the project level (.cursor/mcp.json in your project root) or globally (~/.cursor/mcp.json). Project-level configs take precedence.Can Cursor use MCP tools in inline edits?
How do I verify MCP tools are loaded?
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Connect OpenWeather Agro to Cursor
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.