Model Context Protocol (MCP) defines a shared interface for AI applications to connect with tools and data. Hosts manage client connections to servers, which can expose tools, resources and reusable prompts. The protocol does not itself guarantee privacy or safe actions; those depend on the host, server and deployment.
A single host can maintain connections to multiple servers simultaneously. A user working in Claude Desktop might have one MCP server providing access to their file system, a second connected to their calendar, and a third pulling from a knowledge base. The host coordinates all of them through its client layer, without each server needing to know the others exist.
Tools are functions the AI can execute: run a database query, send a message, write a file, search the web. The AI calls a tool when it needs to take an action or retrieve specific information on demand. Tools require the AI to actively decide when to use them.
Prompts are reusable workflow templates defined by the server: a code review checklist, a meeting summary format, a support ticket structure. They let server providers encode domain-specific workflows that any connected AI can invoke on request.
MCP runs on JSON-RPC 2.0, a lightweight remote procedure call standard that uses structured JSON messages over a shared transport. The AI host sends requests; the MCP server returns responses. The protocol supports both synchronous exchanges and asynchronous notifications for long-running operations.
MCP vs Traditional APIs: What's the Difference?
The most common question about MCP is whether it replaces REST APIs. It does not. The distinction matters for anyone building or evaluating AI tools.
MCP is a layer above that contract. An MCP server often wraps an existing API, but it adds something the raw API cannot provide: a machine-readable description of what the tool does and when an AI should consider using it. The AI discovers available tools dynamically and reasons about when to invoke them, rather than following hardcoded instructions.
A separate clarification on function calling: function calling is a model capability that lets an AI decide to invoke a function during a conversation. MCP is the transport and discovery protocol that defines how that function is exposed across system boundaries. The two complement each other and are not competing approaches.
Enterprise workflow automation uses MCP Tools to link AI to CRM systems, databases, and scheduling APIs. A sales assistant can look up a customer's contract history, check available meeting times, and draft a follow-up email by calling three separate MCP servers in sequence, all coordinated by a single AI host in a single conversation.
Personal agent workflows combine all three primitives together. A personal assistant reads your inbox as a Resource, uses a tool to search your notes for related context, and applies a Prompt template to draft a structured reply, all in a single AI-driven workflow that spans multiple data sources without requiring the user to switch between apps.