Agro MCP. Analyze land metrics and track crop health from satellite data.
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AgroMCP Server monitors farm health using satellite imagery, weather data, and soil metrics. It lets your AI agent track crop growth (NDVI/EVI), monitor soil moisture/temperature, and gather historical weather records for precise agricultural planning.
You can map specific areas using polygons and get real-time data on UV and precipitation.
What your AI agents can do
Create polygon
Creates a new polygon defining an area of interest on the map.
Delete polygon
Removes an existing polygon from the system.
Get accumulated precipitation
Retrieves the total amount of precipitation over a specified time period.
Create, list, and update polygon coordinates to define the exact boundaries of your fields or zones of interest.
Retrieve historical NDVI and EVI data using satellite imagery searches to measure changes in vegetation density and health.
Get real-time and historical soil moisture and temperature readings for specific locations.
Access current, forecast, and historical weather metrics, including accumulated precipitation and temperature totals.
Get information for a specific polygon ID or list all registered polygons across your farm.
Fetch current or forecast UV Index and get the current weather report for a defined location.
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Supported MCP Clients
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Agro MCP Server: 17 Tools for Field Data Analysis
Process satellite imagery, soil data, and historical weather records by calling these specific tools through your AI agent.
019e5cf8create polygon
Creates a new polygon defining an area of interest on the map.
019e5cf8delete polygon
Removes an existing polygon from the system.
019e5cf8get accumulated precipitation
Retrieves the total amount of precipitation over a specified time period.
019e5cf8get accumulated temperature
Gets the total accumulated temperature for a defined area.
019e5cf8get current soil
Retrieves the real-time soil moisture and surface temperature data for a location.
019e5cf8get current uvi
Checks the current Ultraviolet Index level at a given location.
019e5cf8get current weather
Retrieves the current weather conditions and metrics for a specified point.
019e5cf8get forecast uvi
Gets the predicted Ultraviolet Index for a future date.
019e5cf8get forecast weather
Retrieves the predicted weather conditions for a future date.
019e5cf8get historical soil
Fetches soil moisture and temperature data from previous dates.
019e5cf8get historical uvi
Retrieves past Ultraviolet Index readings for a specific location.
019e5cf8get historical weather
Gets archived weather data for a specific time period.
019e5cf8get ndvi history
Retrieves historical NDVI and EVI data using satellite imagery indices.
019e5cf8get polygon
Gets the details and metadata for a single, existing polygon.
019e5cf8list polygons
Lists all the polygons (fields) currently registered in the system.
019e5cf8search imagery
Searches the available database for satellite imagery matching specified criteria.
019e5cf8update polygon
Modifies the boundary coordinates or metadata of an existing polygon.
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What you can do with this MCP connector
AgroMCP Server lets your AI agent monitor farm health using satellite imagery, weather data, and soil metrics. You'll use it to track crop growth, monitor soil moisture and temperature, and gather historical weather records for precise planning. You can map specific areas with polygons and get real-time data on UV and precipitation. Manage Field Boundaries using create_polygon to draw a new area of interest, list_polygons to see all registered fields, and update_polygon to change an existing boundary's coordinates or metadata.
You can also use delete_polygon to clear out old polygons. Track Crop Health Over Time by using get_ndvi_history to pull historical NDVI and EVI data from satellite images, telling you how dense and healthy your crops are over time. You can search_imagery across the database for satellite pictures matching your criteria, and get_polygon pulls the details for a single, known polygon ID. Analyze Soil Conditions by running get_current_soil for real-time soil moisture and surface temperature at a spot, or by using get_historical_soil to pull that data from past dates. Review Weather Data with get_current_weather for the weather right now at a point, or by checking get_forecast_weather for what's coming up.
You can pull accumulated totals using get_accumulated_precipitation for total rainfall over a period, and get_accumulated_temperature for total heat over time. Check UV and Soil Metrics by using get_current_uvi to check the UV Index right now, or get_forecast_uvi for the predicted UV level later. You can also check the past using get_historical_uvi for past UV Index readings, and pull archived weather data with get_historical_weather for any time period.
How Agro MCP Works
- 1 1. Subscribe to the server and enter your AgroMonitoring API Key.
- 2 2. Tell your AI agent what data you need (e.g., 'Show me the NDVI history for the north field').
- 3 3. Your agent calls the correct tool (e.g.,
get_ndvi_history) and sends the coordinates, and you get the raw data back.
The bottom line is: you treat field monitoring like a natural conversation, and your agent handles the complex data calls.
Who Is Agro MCP For?
Agronomists, crop consultants, and environmental data scientists need this. They deal with massive amounts of siloed data—weather feeds, satellite images, soil reports—and waste time manually cross-referencing them. This server lets them query all that data through a single, structured conversation.
Monitors crop health and soil conditions across multiple fields without manually entering data from different sources.
Checks field boundaries and current weather metrics to plan immediate operations like irrigation or pesticide application.
Pulls historical weather and satellite indices to build complex agricultural models or perform comparative analysis.
What Changes When You Connect
- See how crops are really doing. Use
get_ndvi_historyto track NDVI and EVI over time. This shows growth trends, not just single-day snapshots. It pinpoints where the problem is. - Avoid guessing about resources. Check the current soil status with
get_current_soilto get real-time moisture and temperature readings before deciding on irrigation or fertilizer. - Plan around the weather. Instead of just looking at today's forecast, use
get_forecast_weatherandget_accumulated_precipitationto model cumulative effects over weeks. - Manage your assets precisely. Use
list_polygonsandcreate_polygonto ensure every piece of data you pull is tied to the correct, defined field boundary. - Handle historical deep dives. Pull archived data using
get_historical_uviorget_historical_weather. This lets you compare this year's season to last year's data for better planning. - Keep your agent focused. The server has dedicated tools for every data type—soil, weather, polygons, imagery—so your agent knows exactly which function to call.
Real-World Use Cases
Diagnosing a patchy crop yield.
The yield report shows uneven growth. You ask your agent to compare get_ndvi_history for the low-performing zone against the adjacent fields. The agent finds a dip in NDVI corresponding exactly to a period when get_current_soil reported low moisture, pointing directly to a localized irrigation failure.
Planning for a drought season.
You need to know how much water the farm can handle. Your agent first uses list_polygons to define the total area. Then, it runs get_accumulated_precipitation and compares that figure against get_historical_soil to estimate the current water deficit and plan for reduced planting.
Comparing this year's season to last year.
You want to know if the current growing season is better than the last. You run get_historical_weather for both years and compare the total accumulated temperature and precipitation. This gives you a clear, data-backed comparison for stakeholders.
Immediate field scouting.
You arrive on site and need a quick status check. Your agent runs get_current_weather and get_current_uvi for your coordinates. You immediately know if the weather is stable for spraying or if the UV index requires a delay.
The Tradeoffs
Querying data without boundaries
Just asking, 'What is the soil moisture?' without specifying the coordinates or polygon. You get generic, useless data that doesn't apply to your field.
→
Always define the scope first. Use list_polygons to see your fields, then use get_polygon and pass the resulting ID to any data retrieval tool (like get_current_soil) to scope the data correctly.
Mixing up historical tools
Trying to use get_current_uvi to find last month's UV levels. The tool is for real-time data and will fail or give the wrong result.
→
Check the tool name. If you need old data, use the historical versions, like get_historical_uvi or get_historical_weather. Never mix current and historical calls.
Overlooking field geometry
Running complex analyses like get_ndvi_history but forgetting to define the polygon boundaries first. The imagery search fails because the system doesn't know which area to analyze.
→
Start by defining the area. Use create_polygon first, then use the resulting ID when calling search_imagery or get_ndvi_history to scope the analysis.
When It Fits, When It Doesn't
Use this server if your core problem is aggregating diverse, geospatial data (weather, soil, satellite indices) for resource modeling. You need to know where the problem is, and when it happened. You must be able to connect a spatial boundary (polygons) to a time series (NDVI history, historical weather).
Don't use this if you simply need to read a static report or check a single variable in isolation. If all you need is a simple lookup (e.g., 'What is the current temperature?'), a simple weather API might suffice. But if you need to combine that temperature reading with the soil moisture and the historical crop index for a decision, this server is necessary.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AgroMonitoring. 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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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 17 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manual cross-referencing of field reports is a nightmare.
Today, planning a crop cycle means pulling reports from three different dashboards: the weather service for accumulated rain, the GIS platform for field boundaries, and a separate soil lab portal for nutrient levels. You spend hours downloading CSVs, manually matching coordinates, and merging spreadsheets to get a single picture of the field's health.
With the AgroMCP Server, you just ask your agent, 'Compare the last three years of soil moisture to the NDVI history for the North Field.' The agent runs the necessary tools—like `get_historical_soil` and `get_ndvi_history`—and delivers a single, comparative output. You get the answer, not the data dump.
AgroMCP Server: Get full environmental context in one query.
You no longer have to open the weather site for rain totals, then open the soil site for moisture, and finally open the satellite portal for NDVI. You ask for the full picture: 'What was the accumulated precipitation and temperature for the last quarter?'
The server handles the complex chaining of tools—calling `get_accumulated_precipitation` and `get_accumulated_temperature` together—and gives you a single, actionable summary. It's all connected, period.
Common Questions About Agro MCP
How do I use the AgroMCP Server to track crop health? +
You use the get_ndvi_history tool. This tool searches for satellite imagery and retrieves the historical NDVI and EVI data, showing you crop health changes over time.
Can I check the soil moisture history using AgroMCP Server? +
Yes, you use get_historical_soil. This tool fetches soil moisture and temperature data from previous dates, allowing you to track seasonal changes.
How do I define the boundaries for my analysis? +
You start by using create_polygon to define the area of interest. Once you have the polygon ID, you pass that ID to other tools like search_imagery to scope the data correctly.
Does AgroMCP Server support weather forecasting? +
Yes, you use get_forecast_weather or get_forecast_uvi. These tools retrieve predicted weather and UV Index data for planning future operations.
How do I use the `list_polygons` tool to manage my fields? +
You call list_polygons to get a list of all registered areas. This confirms which polygons are currently active in the system, helping you manage your analysis boundaries.
What is the difference between `get_ndvi_history` and `search_imagery`? +
Use get_ndvi_history to pull specific NDVI and EVI data over time for a known area. search_imagery lets you look for available satellite pictures first, which is useful if you don't have a specific date range.
What data does `get_historical_soil` provide? +
It provides historical soil metrics, including moisture and temperature records. This lets you track long-term trends and see how field conditions changed over months or years.
Does the AgroMCP Server handle multiple data types simultaneously? +
Yes, your agent can combine data calls. For example, you can request current weather alongside historical soil metrics for a single polygon.
How do I define a new field for monitoring? +
Use the create_polygon tool. You need to provide a name and the area's geometry in GeoJSON format (coordinates). This polygon ID will then be used for satellite and soil queries.
Can I check the historical health of my crops? +
Yes! Use the get_ndvi_history tool with your Polygon ID and a time range. It returns NDVI and EVI data, which are key indicators of vegetation vigor and growth.
What kind of soil data can I retrieve? +
You can use get_current_soil or get_historical_soil to get moisture levels and surface temperature for a specific polygon, helping you optimize irrigation schedules.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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