EOSDA MCP. Analyze Geospatial Data & Predict Field Outcomes

Listen up. This server connects your AI client directly to massive satellite data sets, letting you monitor everything—crop health, soil moisture, weather patterns—across entire fields from one spot. You're done jumping between GIS software and spreadsheets; your agent acts like a dedicated agronomist that pulls the exact metrics you need when you ask for 'em.

Inventory Field Boundaries. Need to track something? Start by listing or registering specific agricultural areas. Use create_field to write down a new field boundary and its metadata, which lets the system monitor it for future analysis. You can pull up all your existing plots using get_fields, getting boundaries, acreage, crop type, and the current monitoring status for every piece of land you own.

Track Vegetation Trends. To see how well a crop is doing over months or seasons, you'll use time-series data. For general growth tracking—the kind that shows overall health stages—you've got get_ndvi_timeseries, which pulls the Normalized Difference Vegetation Index (NDVI) record for the whole season. If you’re dealing with really dense or tropical canopies, though, you wanna check out get_evi_timeseries; it tracks Enhanced Vegetation Index (EVI) values over time.

For a quick health snapshot at any given moment and location, get_vegetation_index calculates various indices like NDVI, EVI, and NDRE all in one go.

Assess Soil and Water Levels. Knowing what's under the ground is just as important as looking at the sky. You can check current soil moisture levels at multiple depths within any field using get_soil_moisture. To track water stress itself, use get_ndmi_timeseries, which monitors the Normalized Difference Moisture Index (NDMI) over time so you know exactly when and where irrigation is needed.

When you need raw data to build your own models, get_satellite_imagery downloads full satellite images—Sentinel-2 or Landsat—for specific date ranges and fields.

Generate Predictive Maps. You don't just want numbers; you wanna see the picture. The system can generate a map that divides an entire field into distinct zones based on productivity metrics or vegetation health using get_zoning_map. For reports, you can create visual, color-coded images of any index—like NDVI—overlaid directly onto your field boundaries with render_index_map.

You'll also get deep context from the environment. You can access extensive historical weather records—stuff like temperature, wind, and precipitation going back to 1979 using get_weather_data. If you need to plan ahead, get_weather_forecast pulls forward-looking data for agricultural planning, covering periods that stretch up to seven months out. You'll even get current weather predictions by calling get_weather_forecast for a 15-day window.

It’s comprehensive coverage: from historical context to long-term prediction and real-time field status.