Model Context Protocol (MCP)
Model Context Protocol (MCP) is an open protocol that defines how AI applications — particularly large language models — connect to external data sources, APIs, and tools through a standardized interface. Think of it as USB-C for AI integrations: one plug, many devices.
Why MCP Exists
Before MCP, every AI application that needed external data had to build custom integrations for each data source. A developer connecting Claude to John Deere would write completely different code than one connecting it to Climate FieldView. MCP eliminates this N-to-N integration problem by defining a common protocol that any AI client and any data provider can implement.
How It Works
An MCP server exposes two primitives:
- Tools — Functions the AI can call (e.g.,
get_field_boundaries,list_equipment). Each tool has a typed schema describing its inputs and outputs. - Resources — Data the AI can read (e.g., field maps, harvest summaries). Resources are identified by URIs.
The AI client (Claude, GPT, or your own app) connects to one or more MCP servers over a standard transport (HTTP with server-sent events or stdio). The client discovers available tools and resources at connection time, then invokes them as needed during a conversation.
MCP in FieldMCP
FieldMCP is an MCP server that provides unified access to agricultural data platforms. Instead of integrating directly with each provider's proprietary API, you connect your AI application to FieldMCP's MCP gateway, which handles authentication, data normalization, and provider-specific quirks behind a single interface.
The gateway runs in the cloud and supports the full MCP specification including tool discovery, typed invocations, and streaming responses. See the quickstart guide to connect your first AI client, or read the authentication docs to understand how OAuth 2.1 secures the connection.