OpenWeather Agro MCP Server for VS Code Copilot 12 tools — connect in under 2 minutes
GitHub Copilot in VS Code is the most widely adopted AI coding assistant, embedded directly into the world's most popular code editor. With MCP support in Agent mode, Copilot can access external data and APIs to generate context-aware code grounded in real-time information.
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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.
GitHub Copilot Agent mode brings OpenWeather Agro data directly into your VS Code workflow. With a project-scoped config, the entire team shares access to 12 tools. Copilot queries live data, generates typed code, and writes tests from actual API responses, all without leaving the editor.
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 VS Code Copilot 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 VS Code Copilot via MCP
Follow these steps to integrate the OpenWeather Agro MCP Server with VS Code Copilot.
Create MCP config
Create a .vscode/mcp.json file in your project root
Add the server config
Paste the JSON configuration above
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
Start using OpenWeather Agro
Ask Copilot: "Using OpenWeather Agro, help me...". 12 tools available
Why Use VS Code Copilot with the OpenWeather Agro MCP Server
GitHub Copilot for Visual Studio Code provides unique advantages when paired with OpenWeather Agro through the Model Context Protocol.
VS Code is used by over 70% of developers. adding MCP tools to Copilot means your team can leverage external data without leaving their primary editor
Project-scoped MCP configs (`.vscode/mcp.json`) let you commit server configurations to your repository, ensuring the entire team shares the same tool access
Copilot's Agent mode integrates MCP tools seamlessly with file editing, terminal commands, and workspace search in a single agentic loop
GitHub's enterprise compliance and audit features extend to MCP tool usage, providing visibility into how AI interacts with external services
OpenWeather Agro + VS Code Copilot Use Cases
Practical scenarios where VS Code Copilot combined with the OpenWeather Agro MCP Server delivers measurable value.
Live API integration: Copilot can query an MCP server, inspect the response schema, and generate typed API client code in the same step
DevSecOps workflows: security teams can give developers access to domain intelligence tools directly in their editor for real-time vulnerability assessment during code review
Data pipeline development: Copilot fetches sample data via MCP and generates transformation scripts, validators, and test fixtures from actual API responses
Documentation generation: Copilot queries available tools and auto-generates README sections, API reference docs, and usage examples
OpenWeather Agro MCP Tools for VS Code Copilot (12)
These 12 tools become available when you connect OpenWeather Agro to VS Code Copilot 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 VS Code Copilot
Ready-to-use prompts you can give your VS Code Copilot 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 VS Code Copilot
Common issues when connecting OpenWeather Agro to VS Code Copilot through the Vinkius, and how to resolve them.
MCP tools not available
OpenWeather Agro + VS Code Copilot FAQ
Common questions about integrating OpenWeather Agro MCP Server with VS Code Copilot.
Which VS Code version supports MCP?
How do I switch to Agent mode?
Can I restrict which MCP tools Copilot can access?
Does MCP work in VS Code Remote or Codespaces?
.vscode/mcp.json work in Remote SSH, WSL, and GitHub Codespaces environments. The MCP connection is established from the remote host, so ensure the server URL is accessible from that environment.Connect OpenWeather Agro with your favorite client
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Connect OpenWeather Agro to VS Code Copilot
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.