# EOSDA MCP for AI Agents AI Agent Connect > EOSDA MCP gives your AI agent direct access to satellite-based crop monitoring and precision agriculture data. It lets you track vegetation health, analyze soil moisture, pull historical weather records, and generate production zoning maps for large-scale farming. ## Overview - **Category:** the-unthinkable - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_W1riYytnYABpHWpaWtxCVS18WzgQkjJnAYcVAx7d/ai-agent-connect - **Tags:** satellite-imagery, crop-monitoring, precision-agriculture, vegetation-indices, soil-moisture, remote-sensing ## Description Managing a farm used to mean jumping between specialized GIS software, weather apps, and manual satellite data downloads. You would spend hours trying to figure out if a dip in vegetation color meant a pest issue or just a lack of water. This Connector changes that by putting all that data into a single conversation with your AI agent. Instead of hunting for data, you just ask questions about your fields. You can check how your crops are actually growing, see where the soil is dry, and get accurate weather forecasts to plan your next move. It handles the heavy lifting of processing satellite imagery so you can focus on making decisions. Since Vinkius makes it so easy to plug this into your existing workflow, you can move from raw data to actionable insights in seconds. It is like having a dedicated agronomist who never sleeps, constantly watching your land from space and telling you exactly what needs attention right now. ## Tools ### get_ndmi_timeseries Get NDMI values over time to monitor crop water stress and irrigation needs. ### get_evi_timeseries Retrieve EVI data to monitor dense canopies and high-biomass tropical crops. ### get_fields List all fields in your account including their boundaries, areas, and current growth stages. ### get_ndvi_timeseries Get NDVI trends to track overall vegetation health and growth curves across a season. ### render_index_map Create a visual map of a field with color-coded vegetation index values for reports. ### get_satellite_imagery Download raw imagery from sources like Sentinel-2 or Landsat for specific dates. ### get_soil_moisture Check soil moisture at different depths to optimize irrigation timing. ### get_vegetation_index Calculate specific indices like NDRE or MSAVI to assess different crop health markers. ### get_weather_data Pull historical weather parameters like temperature and solar radiation dating back to 1979. ### get_weather_forecast Get 15-day to 7-month weather forecasts for long-term agricultural planning. ### get_zoning_map Generate production and health zoning maps for variable rate application planning. ### create_field Register a new field for satellite monitoring by providing its boundary and crop type. ## Prompt Examples **Prompt:** ``` Is my soybean field getting enough water right now? ``` **Response:** ``` Checking moisture levels for your soybean field (ID: 67890)... **Current Soil Status:** * **Surface Moisture:** 38% * **Root Zone:** 42% * **Status:** Adequate for current growth stage. **Weather Outlook:** * **Next 3 Days:** Sunny, 28-32°C, no rain. * **Days 4-7:** 60% chance of 15mm rainfall. **Recommendation:** Based on current moisture and the expected rain in 4 days, you can delay irrigation for another 72 hours. ``` **Prompt:** ``` Show me the growth trend for the wheat field over the last 3 months. ``` **Response:** ``` Fetching NDVI trends for the wheat field (ID: 11223) from the last 90 days... | Month | Avg NDVI | Growth Phase | | :--- | :--- | :--- | | May | 0.32 | Early Growth | | June | 0.58 | Canopy Closure | | July | 0.74 | Peak Vegetation | | August | 0.68 | Grain Fill | **Summary:** The field reached peak vegetation in mid-July. Growth is currently in the grain fill stage with a slight decline in NDVI as expected. ``` **Prompt:** ``` Create a map of the high-productivity areas in my corn field. ``` **Response:** ``` Generating a productivity zoning map for your corn field (ID: 12345)... I have analyzed the vegetation health and divided the field into 4 management zones: * **Zone 1 (Very High):** 22% of field (Avg NDVI 0.82) * **Zone 2 (High):** 31% of field (Avg NDVI 0.71) * **Zone 3 (Medium):** 28% of field (Avg NDVI 0.58) * **Zone 4 (Low):** 19% of field (Avg NDVI 0.41) **Action Item:** Zone 4 shows significantly lower productivity and may require soil amendments or increased nitrogen application. ``` ## Capabilities ### Track crop growth trends Get a clear picture of how your vegetation is developing over a full growing season. ### Monitor soil moisture levels Check moisture at different depths to decide exactly when to turn on the irrigation. ### Generate production zoning maps Create maps that show which parts of your field need more fertilizer or water. ### Access historical weather records Pull over 20 years of weather data to help predict future yields and risks. ### Retrieve raw satellite imagery Get direct download links for imagery from sources like Sentinel-2 and Landsat. ### Calculate vegetation health indices Get specific data points like NDVI, EVI, and NDRE to monitor various crop types. ## Use Cases ### Identifying yellowing corn A farm manager notices a patch of yellowing corn. They ask the agent to check the NDVI trend for that specific field to see if it is a moisture issue. ### Irrigation scheduling An agronomist wants to know if it is time to irrigate. The agent checks get_soil_moisture and compares it to the 15-day get_weather_forecast. ### Client reporting A consultant needs to show a client the best parts of a field. The agent uses render_index_map to create a visual health report. ### Onboarding new plots A farm owner wants to add a new 50-acre plot to their dashboard. They give the agent the coordinates, and it uses create_field to start monitoring. ## Benefits - Stop guessing about irrigation with real-time soil moisture data from get_soil_moisture. - Identify crop stress weeks earlier by tracking trends with get_ndvi_timeseries. - Save hours on manual GIS work by generating shareable maps through render_index_map. - Plan your entire season with confidence using 7-month weather forecasts from get_weather_forecast. - Optimize fertilizer use by creating precise management zones with get_zoning_map. - Monitor high-biomass crops more accurately using get_evi_timeseries instead of standard indices. ## How It Works The bottom line is you get satellite-driven farm intelligence without ever touching a GIS tool. 1. Subscribe to the EOSDA MCP on Vinkius. 2. Add your EOSDA API key from your account settings to your AI client. 3. Ask your agent to check your field health, moisture levels, or weather forecasts. ## Frequently Asked Questions **Can I use the EOSDA MCP to track my crops from space?** Yes, this Connector connects your AI agent to satellite data so you can monitor crop health, growth trends, and vegetation indices for your entire farm without leaving your desk. **How does the EOSDA MCP help with irrigation?** It pulls real-time soil moisture data at different depths and combines it with 15-day weather forecasts to help you decide exactly when to water your fields. **Can the EOSDA MCP show me historical weather?** Yes, the Connector can access over 20 years of historical weather data, including temperature, rainfall, and solar radiation, to help with long-term planning. **Can I add my own fields to the EOSDA MCP?** You can register new fields by providing their boundaries and crop types. The AI agent will then include them in your satellite monitoring and analysis. **Does the EOSDA MCP help with precision farming?** It is built for precision agriculture. It can generate zoning maps that identify high and low productivity areas for variable rate application of water and fertilizer. **Can I get visual maps of my field health?** Yes, you can ask your agent to render visual maps of your fields with custom color-coded indices to use in reports or for stakeholder meetings. **Can my AI calculate NDVI for my corn field and show me the vegetation health trend over the growing season?** Yes! Use the `get_ndvi_timeseries` tool with your field ID and the growing season date range (e.g., date_from=2025-04-01, date_to=2025-10-31). This returns NDVI values for each satellite overpass, showing vegetation health progression from planting through harvest. You can also use `get_vegetation_index` with index=NDVI for point-in-time analysis, or `render_index_map` to generate a visual color-coded NDVI map of your field. **How do I get weather forecasts and soil moisture data to plan irrigation for my fields?** Use `get_weather_forecast` with your field ID and forecast_range=15_days or 1_month to get upcoming precipitation and temperature forecasts. Combine this with `get_soil_moisture` to check current soil moisture levels at root zone depth. Together these tools help you determine if and when irrigation is needed. For historical context, use `get_weather_data` with past dates to understand rainfall patterns and evapotranspiration trends. **Can I generate a zoning map to identify low and high productivity areas within my field?** Yes! Use the `get_zoning_map` tool with your field ID. You can specify the vegetation index (NDVI is most common), number of zones (3-5 recommended), and date for analysis. The API returns zone boundaries, average index values per zone, area percentages, and management recommendations. This is essential for variable rate application (VRA), precision fertilization, and targeted irrigation planning.