Farmonaut 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": {
"farmonaut": {
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}
}
}
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About Farmonaut MCP Server
Connect your Farmonaut Satellite API to any AI agent and take full control of satellite-based crop monitoring, vegetation index analysis, weather tracking, AI crop advisory, and deforestation detection through natural conversation.
GitHub Copilot Agent mode brings Farmonaut 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
- Field Management — List and register agricultural fields with boundaries, crop types, and planting dates
- NDVI Analysis — Calculate NDVI from Sentinel-2, Landsat, and PlanetScope for crop health monitoring
- NDWI Water Index — Monitor crop water content and irrigation needs with water index analysis
- EVI Enhanced Index — Track enhanced vegetation index for high-biomass and dense canopy crops
- Weather Data — Access historical and current weather data for agricultural decision making
- Weather Forecast — Get forecasts from 7 days to 3 months for agricultural planning
- Soil Moisture — Monitor soil moisture at different depths for irrigation scheduling
- Satellite Imagery — Retrieve true-color, false-color, and NDVI overlay images from multiple satellites
- AI Crop Advisory — Get AI-powered recommendations for irrigation, fertilizer, pest control, and harvest
- Deforestation Alerts — Detect land use changes and tree cover loss for conservation compliance
- SAR Analysis — All-weather monitoring using Synthetic Aperture Radar that penetrates clouds
- Multi-Satellite Support — Access Sentinel-2, Landsat, PlanetScope, and SAR satellite data
The Farmonaut 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 Farmonaut to VS Code Copilot via MCP
Follow these steps to integrate the Farmonaut 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 Farmonaut
Ask Copilot: "Using Farmonaut, help me...". 12 tools available
Why Use VS Code Copilot with the Farmonaut MCP Server
GitHub Copilot for Visual Studio Code provides unique advantages when paired with Farmonaut 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
Farmonaut + VS Code Copilot Use Cases
Practical scenarios where VS Code Copilot combined with the Farmonaut 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
Farmonaut MCP Tools for VS Code Copilot (12)
These 12 tools become available when you connect Farmonaut to VS Code Copilot via MCP:
add_field
Accepts field boundary as GeoJSON polygon or coordinates, field name, crop type, and planting date. Returns the created field with ID, calculated area, and monitoring activation status. Essential for onboarding new fields into the monitoring system, expanding farm coverage, and setting up new crop seasons. AI agents should use this when users ask "add a new field for monitoring", "register this field boundary", or need to set up satellite monitoring for a new agricultural area. Register a new agricultural field for satellite monitoring
get_crop_advisory
Returns recommendations for irrigation, fertilization, pest control, harvest timing, and field operations. Essential for data-driven farm management, precision agriculture, and optimizing crop inputs. AI agents should use this when users ask "what should I do in my field this week", "get irrigation and fertilizer recommendations", or need AI-powered crop management advice. Get AI-powered crop management advisories and recommendations
get_deforestation_alerts
Uses satellite imagery to detect tree cover loss, land clearing, and vegetation changes over time. Essential for conservation compliance, environmental monitoring, carbon credit verification, and land use change detection. AI agents should reference this when users ask "show deforestation alerts in my area", "detect land use changes", or need environmental compliance monitoring. Get deforestation and land change detection alerts
get_evi
EVI is more sensitive in high-biomass regions and less affected by atmospheric conditions and soil background than NDVI. Essential for monitoring dense canopies, tropical crops, and areas with high atmospheric interference. Returns EVI values, statistics, satellite source, and acquisition dates. AI agents should use this when users ask "show me EVI trends for this field", "how is the canopy developing in high-biomass areas", or need enhanced vegetation index analysis for dense vegetation. Calculate EVI enhanced vegetation index for high-biomass crop monitoring
get_fields
Returns field names, boundaries (GeoJSON polygons), area in hectares/acres, crop type, planting dates, and current monitoring status. Essential for farm management overview, field inventory, and selecting target fields for satellite analysis. AI agents should use this when users ask "show me all my fields", "list monitored fields", or need to identify available fields for vegetation index or weather queries. List all agricultural fields monitored in your Farmonaut account
get_ndvi
NDVI measures vegetation health and vigor on a scale of -1 to 1, with higher values indicating healthier vegetation. Returns NDVI values, statistics (mean, min, max, std), satellite source, acquisition date, and cloud cover percentage. Essential for crop health assessment, growth stage monitoring, stress detection, and yield prediction. AI agents should use this when users ask "what is the NDVI for my rice field this month", "calculate vegetation health for field X", or need NDVI-based crop health analysis. Calculate NDVI vegetation index for crop health monitoring
get_ndwi
NDWI is sensitive to vegetation water content and soil moisture, making it essential for irrigation scheduling, drought monitoring, and water stress detection. Returns NDWI values, statistics, satellite source, and acquisition dates. AI agents should reference this when users ask "what is the water content in my crops", "do I need to irrigate", or need water stress analysis for irrigation planning. Calculate NDWI water index for crop water stress and irrigation monitoring
get_sar_analysis
SAR penetrates clouds and works day/night, making it essential for monitoring in cloudy or rainy conditions. Returns backscatter values, soil moisture estimates, crop structure information, and change detection analysis. Essential for all-weather monitoring, flood detection, soil moisture mapping, and crop structure analysis. AI agents should use this when users ask "get SAR analysis for my field during cloudy season", "monitor crops through cloud cover", or need all-weather satellite analysis. Get Synthetic Aperture Radar (SAR) analysis for all-weather crop monitoring
get_satellite_images
Returns true-color and false-color composites, NDVI overlays, and raw spectral bands. Essential for visual crop assessment, change detection, damage assessment, and downloading imagery for further processing. AI agents should reference this when users ask "show me satellite images of my field from last week", "get latest Sentinel-2 imagery", or need satellite imagery for visual assessment. Retrieve satellite imagery for agricultural fields from multiple sources
get_soil_moisture
Returns soil moisture levels at different depths (surface, root zone, deep soil), moisture anomalies, and irrigation recommendations. Essential for irrigation scheduling, drought monitoring, water stress detection, and water resource optimization. AI agents should use this when users ask "what is the soil moisture level in my field", "do I need to irrigate", or need soil moisture data for irrigation planning. Get soil moisture data for irrigation scheduling and drought monitoring
get_weather
Includes temperature (air, soil), precipitation, humidity, wind speed/direction, solar radiation, evapotranspiration, and growing degree days. Essential for irrigation planning, frost risk assessment, disease/pest pressure modeling, and yield prediction. AI agents should use this when users ask "what was the weather like on my field last month", "get temperature and rainfall data", or need historical weather analysis for crop management decisions. Get historical and current weather data for agricultural fields
get_weather_forecast
Includes temperature, precipitation, humidity, wind, and solar radiation forecasts. Essential for planting schedule optimization, harvest timing, irrigation planning, frost protection, and seasonal crop management. AI agents should reference this when users ask "what is the weather forecast for my field next week", "get seasonal precipitation forecast", or need forward-looking weather data for agricultural planning. Get weather forecasts for agricultural planning and irrigation scheduling
Example Prompts for Farmonaut in VS Code Copilot
Ready-to-use prompts you can give your VS Code Copilot agent to start working with Farmonaut immediately.
"Show me the NDVI trend for my rice field over the last 3 months."
"What is the 7-day weather forecast and current soil moisture for my wheat field?"
"Get AI crop advisory recommendations for my cotton field this week."
Troubleshooting Farmonaut MCP Server with VS Code Copilot
Common issues when connecting Farmonaut to VS Code Copilot through the Vinkius, and how to resolve them.
MCP tools not available
Farmonaut + VS Code Copilot FAQ
Common questions about integrating Farmonaut 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 Farmonaut with your favorite client
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Connect Farmonaut to VS Code Copilot
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.