| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Exposes weather data through MCP protocol rather than direct API calls, allowing LLM agents to reason about weather conditions in natural language and chain weather checks into multi-step fieldwork planning workflows without manual API integration.
vs alternatives: Simpler than building custom weather integrations; MCP abstraction lets non-technical users query weather via conversational AI without writing API code.
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.
Unique: Wraps geocoding APIs in MCP tools, enabling LLM agents to resolve farm locations conversationally and integrate location context into multi-step agricultural workflows without direct API calls.
vs alternatives: More accessible than raw geocoding APIs for non-technical users; MCP abstraction allows natural language queries like 'what field am I in?' instead of manual coordinate conversion.
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.
Unique: Integrates geometric area calculation directly into MCP tool interface, allowing LLM agents to compute field areas from coordinate inputs and chain results into yield or cost estimation workflows without manual calculation.
vs alternatives: Faster than manual GIS software for quick area estimates; MCP integration allows conversational queries like 'how many acres is my field?' with automatic unit conversion.
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.).
Unique: Encodes crop-specific agronomic guidelines in MCP tools, enabling LLM agents to recommend planting densities and calculate seed requirements conversationally without requiring farmers to consult separate agronomic references.
vs alternatives: More accessible than agronomic tables or spreadsheets; MCP integration allows natural language queries like 'how many corn seeds do I need?' with automatic calculation and validation.
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.
Unique: Integrates yield estimation models into MCP tools, allowing LLM agents to generate yield forecasts conversationally and incorporate yield data into multi-step planning workflows (harvest logistics, revenue projections, field comparisons).
vs alternatives: Simpler than building custom yield models; MCP abstraction lets farmers ask 'what yield should I expect?' and get estimates without manual calculation or external tools.
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.
Unique: Provides agricultural-specific unit conversion (bushels, hectares, plants/acre) through MCP tools, enabling LLM agents to handle mixed-unit inputs and normalize data without manual conversion tables.
vs alternatives: More comprehensive than generic unit converters; includes agriculture-specific units (bushels, plants/hectare) and allows seamless conversion in LLM workflows.
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.
Unique: Embeds crop agronomic knowledge in MCP tools, enabling LLM agents to provide crop-specific guidance conversationally without requiring users to consult separate agronomic references or databases.
vs alternatives: More integrated than external crop databases; MCP tools allow natural language queries like 'when should I plant corn?' with automatic lookup and context-aware recommendations.
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.
Unique: Integrates agronomic time calculations (GDD, phenology) into MCP tools, enabling LLM agents to estimate crop maturity and plan harvest timing conversationally without manual GDD calculations.
vs alternatives: More specialized than generic date calculators; includes agricultural phenology models and GDD calculations for crop-specific development tracking.
+1 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs Farmerconnect-mcp at 31/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities