Understanding the Model Context Protocol (MCP): Complete Guide
type: NewsArticle
description: Anthropic has developed the Model Context Protocol (MCP), an open-source protocol that enables AI models to directly access external systems such as databases, files, and internal tools for fresh and accurate context. The protocol uses JSON-RPC communication between MCP servers, which expose data and functionality, and MCP clients like Claude or Gemini that connect to retrieve information. This eliminates the need for manual data copying and reformatting by creating a standardized bridge between artificial intelligence and existing enterprise systems.
date published: 2026-05-28 10:15:04+00:00
date modified: 2026-05-28 10:22:50.361171+00:00
language: en
is accessible for free: true
article body: Imagine being able to give your favorite AI direct and secure access to your databases, files, or internal systems. No more copying and pasting information or manually reformatting your data. This is exactly what the **Model Context Protocol (MCP)** developed by Anthropic offers. In my development practice with AI since the emergence of Claude, I’ve often been frustrated by the need to manually “feed” models with context. MCP is a game-changer by creating a real bridge between artificial intelligence and our existing systems. The **Model Context Protocol (MCP)** is an open-source protocol developed by Anthropic that allows AI models to directly access external systems to obtain fresh and accurate context. Think of it as an **API specifically designed for artificial intelligence**. To understand the value of MCP, imagine working in an international company. You need information stored in different departments (accounting, HR, technical), but each department speaks a different language. MCP plays the role of a universal translator that: Before MCP, for an AI to access your data, you had to: With MCP, AI can: MCP is built around two main components that communicate via the JSON-RPC protocol: The **MCP server** is what you develop. It: Concrete example: An MCP server for the Montpellier tram network could expose: The **MCP client** is the artificial intelligence (Claude, Gemini, VS Code Copilot) that: MCP uses **JSON-RPC** as its communication protocol. Each exchange follows a simple structure: ``` { "jsonrpc": "2.0", "method": "method_name", "params": { "parameter1": "value1", "parameter2": "value2" }, "id": "unique_identifier" } ``` This approach offers several advantages: When an AI connects to your MCP server, here’s what happens: The first request is always of type `initialize` : ``` { "jsonrpc": "2.0", "method": "initialize", "params": { "protocolVersion": "2025-06", "capabilities": {}, "clientInfo": { "name": "Claude", "version": "3.5" } }, "id": 1 } ``` Your ser…
publisher: Web Pulse
keywords: artificial-intelligence, ai-tools, ai-infrastructure, large-language-models, ai-agents
mentions: Anthropic
mentions: Claude
mentions: Model Context Protocol
mentions: MCP
mentions: Gemini
mentions: VS Code Copilot
mentions: JSON-RPC
citation: CreativeWork