EOSDA MCP. Translate Satellite Data into Field Action.
EOSDA connects advanced satellite imagery, weather data, and soil moisture readings directly into your AI agent. Monitor crop health trends across entire growing seasons by calculating key vegetation indices, generating visual zoning maps, and forecasting field conditions—all from a natural conversation.
Give Claude and any AI agent real-world access
Calculates core vegetation health indices (NDVI, EVI) and tracks trends across the entire growing season.
Retrieves current soil moisture levels and generates drought impact assessments using specialized indices like NDMI.
Accesses long-range weather forecasts (up to 7 months) and historical climate data for risk assessment.
Generates color-coded zoning maps that segment fields by productivity or vegetation health for variable rate application planning.
List, register, and manage multiple agricultural fields with their specific boundaries, crop types, and planting dates.
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What AI agents can do with EOSDA: 12 Advanced Agriculture Tools
These tools allow you to manage the entire data lifecycle for precision agriculture—from registering fields to generating final, actionable productivity maps.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using EOSDA MCPGet Ndmi Timeseries
Retrieves time series data for NDMI, which helps track crop water stress and optimize irrigation timing.
Create Field
Registers new agricultural fields into the monitoring system using GeoJSON...
Get Evi Timeseries
Calculates time series data for EVI, ideal for tracking canopy development in...
Get Fields
Lists all registered monitored fields, providing boundaries, area sizes, crop types...
Get Ndvi Timeseries
Provides time series data for NDVI, which tracks overall vegetation health trends...
Render Index Map
Creates shareable visual maps that overlay color-coded vegetation index values onto specific field boundaries.
Get Satellite Imagery
Retrieves raw satellite images from sources like Sentinel-2 and Landsat, including metadata and cloud cover percentages.
Get Soil Moisture
Gathers soil moisture readings at multiple depths, along with recommendations for...
Get Vegetation Index
Calculates various vegetation indices (NDVI, EVI, NDRE, etc.) for a field and date...
Get Weather Data
Accesses extensive historical weather data, including temperature, rainfall, and...
Get Weather Forecast
Provides forward-looking weather forecasts for the field, covering everything from...
Get Zoning Map
Generates detailed zone boundaries and average index values needed for precise fertilization or targeted irrigation plans.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with EOSDA, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by EOSDA. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Sandboxed per request
Zero-Trust Proxy
No stored credentials
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Policy on each call
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EU data residency
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~60% cost reduction
The Daily Grind of Field Analysis
Today, assessing a farm's health means jumping between at least five different platforms: the weather service dashboard, the satellite imagery portal, the soil mapping database, and your internal spreadsheet. You download raw band files, calculate an index in Excel, then upload that to another system just to generate a final map. It’s tedious, slow, and you lose time cross-referencing data sets.
With this MCP connected through Vinkius, those steps disappear. You just tell your agent what you want—say, 'How is the crop doing right now?' The AI handles the sequence: it pulls raw imagery, runs multiple calculations like get_vegetation_index, compares historical trends using get_ndvi_timeseries, and spits out a clear summary without you touching a single dashboard.
Generating Productivity Zoning Maps
Before, creating management zones required specialized GIS software and deep knowledge of variable rate application (VRA) protocols. You had to manually define thresholds and run complex spatial queries across different layers.
Now, you simply ask for a zoning map using get_zoning_map. The MCP processes the data, defines productivity levels based on your chosen index, and returns a ready-to-use map that tells you exactly where to focus resources.
What EOSDA MCP does for your AI
Need to analyze what's really happening in the fields? This MCP lets you bypass complex GIS software and massive datasets. You connect your agent through Vinkius, and suddenly, satellite data becomes conversational. You can ask your AI client things like, 'How has the moisture content changed since last month?' or 'Show me a map of where the productivity is lowest.' It handles everything: retrieving raw imagery from multiple sources, calculating advanced vegetation indices (like NDVI and EVI) over time, pulling historical weather records spanning decades, and even generating precise zoning maps for targeted treatments.
Forget manual data pulls and spreadsheet cross-referencing. Your AI agent acts as a dedicated precision ag analyst, giving you actionable insights on everything from irrigation needs to yield predictions.
019d7591-146b-7066-ae2d-a7a9a508481c How to set up EOSDA MCP
The bottom line is that your AI client uses this MCP to turn massive, siloed scientific datasets into simple instructions for farm managers.
Subscribe to this MCP and enter your API key in the Vinkius platform.
Tell your AI agent what you need—for example, 'Show me the NDVI trend for my corn field.'
The agent runs the necessary tool calls, fetches raw satellite data, calculates indices, and returns a plain language summary with actionable results.
Who uses EOSDA MCP
This is for the agronomist who spends hours cross-referencing weather reports with satellite imagery. It's for the farm manager tired of guessing about irrigation needs and the consultant needing instant, verifiable field productivity data.
Analyzes vegetation indices (like NDRE or EVI) to detect early signs of crop stress across different growth stages.
Uses the MCP to schedule irrigation cycles by monitoring soil moisture levels and checking multi-month weather forecasts for a specific field.
Generates detailed productivity zoning maps and historical trend reports to advise clients on variable rate fertilization or planting strategies.
Benefits of connecting EOSDA MCP
You stop guessing about crop health. By using get_ndvi_timeseries or get_evi_timeseries, your agent shows the exact progression of vegetation vigor across seasons.
Irrigation planning gets precise. The get_soil_moisture tool gives you depth-specific readings and tells you if rain is necessary before you waste water or money.
Forecasting risk used to mean checking five different websites. Now, get_weather_forecast pulls multi-month predictions (up to 7 months) directly into your workflow for planning planting schedules.
Mapping complex data points is simple. Instead of exporting raw raster files, render_index_map generates polished, color-coded visualizations ready for stakeholder reports.
The ability to create a new field record using create_field means you never have to manually set up monitoring boundaries again; just define the area and monitor.
EOSDA MCP use cases
Detecting hidden water stress
A farm manager noticed poor yield estimates. They ask their agent, 'What is happening with moisture?' The agent uses get_soil_moisture and get_ndmi_timeseries to pinpoint that the root zone has been critically dry since last week, allowing immediate scheduling of targeted irrigation.
Optimizing variable rate application
An agricultural consultant needs a fertilization plan. They ask for 'productivity zones.' The agent runs get_zoning_map using current NDVI data, generating four distinct zones that tell the client exactly where to increase or decrease fertilizer use.
Planning for seasonal risks
A farmer needs to decide when to plant a high-risk crop. They ask their agent for 'weather outlook.' The agent pulls get_weather_forecast, showing a 60% chance of frost in the next three weeks, letting the farmer delay planting until conditions stabilize.
Comparing year-over-year growth
An agronomist wants to check if this season's crop is doing better than last. They ask for 'NDVI trend comparison.' The agent uses get_ndvi_timeseries and compares the current curve against historical data, highlighting where growth peaked or stalled.
EOSDA MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Using generic APIs
Calling a general weather API just to check rainfall. You get raw numbers but no context on soil saturation or crop stage.
Use the specific get_weather_data tool, which provides over 1800 parameters and correlates historical climate data with your registered field boundaries.
Manual GIS software
Having to open QGIS, load multiple band layers (Sentinel-2, Landsat), calculate NDVI manually, and then export a map.
Use render_index_map. Your agent handles the multi-band calculation and renders the finished, color-coded visualization instantly.
Ignoring field boundaries
Running an index calculation that covers multiple properties or areas you don't manage, leading to useless data.
Always start by using get_fields to list your monitored plots, and then specify the target area when running any analysis tool like get_vegetation_index.
When to use EOSDA MCP
Use this MCP if you need a deep, multi-layered analysis that combines geospatial data (satellite imagery), time-series trends, and long-term climate modeling into one conversational output. Don't use it if you just need to check today's temperature—use a simple weather widget instead. If your goal is simply tracking market prices or managing payroll, this isn't for you; stick to finance tools. However, if you are analyzing the physical health of crops and land, especially when comparing different indices like NDVI vs EVI, this MCP provides the necessary depth and breadth that simple data dashboards miss. It’s your single point of truth for field metrics.
Frequently asked questions about EOSDA MCP
How do I start monitoring my fields with EOSDA? Using get_fields? +
You first use the get_fields tool to list your existing monitored plots. This gives you a baseline inventory, including boundaries and current crop types, so your agent knows what data to pull for analysis.
Can I compare different vegetation indices with EOSDA? Using get_vegetation_index? +
Yes, this MCP supports over 17 indices. You can ask the agent to calculate NDVI alongside EVI and NDRE simultaneously to get a multi-faceted view of crop health.
What if I need historical weather data? Does EOSDA support it? +
The get_weather_data tool accesses records spanning back decades. You can analyze temperature, precipitation, and other parameters from 1979 onward for deep seasonal comparisons.
Is the soil moisture data real-time? Can I use get_soil_moisture? +
The get_soil_moisture tool provides readings at various depths, helping you schedule irrigation. It gives you specific data points for root zone monitoring rather than just a general estimate.
How far in advance can I plan with this MCP? Using get_weather_forecast? +
The system is robust enough to provide forecasts ranging from 15 days out all the way up to seven months. This helps optimize seasonal planting and harvesting windows.