Farmerconnect-mcp

Integrates real-time weather data APIs (likely OpenWeatherMap or similar) with geographic coordinates to retrieve location-specific forecasts, temperature ranges, precipitation probability, and wind conditions. The MCP server exposes weather tools that accept latitude/longitude inputs and return structured forecast data suitable for decision-making on planting, spraying, or harvesting windows. Architecture uses standard REST API calls to weather providers with caching to minimize rate-limit consumption.

Provides reverse geocoding and place name resolution to convert coordinates into human-readable farm/field names, nearby landmarks, or administrative regions. Likely uses a geocoding API (Google Maps, Nominatim, or similar) to resolve latitude/longitude to place names and vice versa. Returns structured location metadata (address, region, country) useful for logging fieldwork activities or identifying specific farm parcels in multi-location operations.

Computes the area of farm parcels or fields given geographic boundaries (polygon coordinates, bounding boxes, or field perimeters). Uses geometric algorithms (likely Shoelace formula or similar) to calculate area in multiple units (hectares, acres, square meters). Accepts coordinate arrays representing field boundaries and returns precise area measurements suitable for yield calculations, input cost estimation, or regulatory reporting.

Calculates optimal plant spacing, population density, and row configurations based on crop type, field area, and agronomic guidelines. Takes inputs like desired plants-per-hectare, row spacing, and seed rate to compute planting requirements and validate spacing against crop-specific recommendations. Likely uses a crop database or lookup table with standard density parameters for common crops (corn, soybeans, wheat, etc.).

Estimates crop yield based on field area, plant density, historical yield data, and optional environmental factors (weather, soil quality). Uses regression models or lookup tables correlating input parameters to expected yield per unit area. Returns yield estimates in standard units (bushels/acre, tons/hectare) suitable for revenue projections, harvest planning, or performance benchmarking.

Converts between common agricultural units (area: acres/hectares/square meters, weight: pounds/kilograms/tons, volume: bushels/liters, density: plants/acre or plants/hectare, yield: bushels/acre or tons/hectare). Implements a unit registry with conversion factors for standard agricultural measurements, allowing bidirectional conversion with optional precision control.

Provides structured crop data including planting windows, maturity periods, water requirements, nutrient needs, pest/disease susceptibility, and yield potential for common crops. Likely uses a crop database (possibly embedded or API-backed) with agronomic parameters indexed by crop name or code. Returns reference information suitable for decision support, planning, and educational purposes.

Provides date/time calculations for farm scheduling, including growing degree days (GDD), days to maturity, planting window validation, and phenological stage estimation. Implements calendar utilities and optional integration with weather data to calculate accumulated heat units (GDD) for crop development tracking. Supports timezone handling for multi-location operations.

Chains multiple MCP tools together to support end-to-end fieldwork planning workflows: weather checks → field identification → area calculations → planting density/yield estimates → scheduling. Leverages MCP's tool-calling architecture to enable LLM agents to reason through complex planning scenarios, validate inputs across steps, and generate actionable fieldwork recommendations. Supports conversational planning where users describe goals and the agent decomposes into tool calls.