MCP

Model Context Protocol – Overview

Official MCP overview, lifecycle, transports, server and client features.

Overview

The Model Context Protocol (MCP) is a standardized way for AI assistants to securely connect to data sources and tools. It consists of several key components: Base Protocol (JSON-RPC message types), Lifecycle Management (connection initialization and capability negotiation), Server Features (resources, prompts, and tools), Client Features (sampling and root directory lists), and Utilities (logging and argument completion).

Key Points

Built on JSON-RPC 2.0 specification for message communication

Modular design allows implementations to support only needed features

All implementations MUST support base protocol and lifecycle management

Server features include resources, prompts, and tools

Client features include sampling and root directory management

Clear separation of concerns between different protocol layers

Prerequisites

Understanding of JSON-RPC 2.0 specification

Basic knowledge of client-server architecture

Familiarity with AI assistant integration patterns

Step-by-Step Guide

1Understand the base protocol and message types

2Implement lifecycle management for connections

3Choose which server features to support (resources, prompts, tools)

4Implement client features as needed (sampling, roots)

5Add utilities for logging and argument completion