MCP (Model Context Protocol) is the standardization protocol open-sourced by Anthropic in November 2024. Its core problem: how to connect AI models to external tools and data sources in a unified way, rather than writing custom integration code for each tool. The protocol quickly gained broad industry adoption after launch, with OpenAI, Google DeepMind, and other major AI companies announcing support.
## Architecture
MCP uses a client-server architecture:
**MCP Host**: the application running the AI model — Claude Desktop, Cursor IDE, custom agent applications. The host initiates MCP connections.
**MCP Client**: embedded in the host application, manages 1:1 connections to MCP servers, handles protocol communication.
**MCP Server**: a lightweight service exposing specific capabilities. Each server focuses on one tool or data source category — filesystem server, database server, Slack server, GitHub server.
**Three capability types**: Tools (callable functions — “search web”, “execute SQL”, “send email”); Resources (readable data — file contents, database records, API responses); Prompts (reusable prompt templates provided by the server).
## Why MCP Matters
**The prior problem**: each AI application needed custom integration code per tool. Connecting Slack required Slack API code; connecting GitHub required GitHub API code. Every integration was bespoke and non-reusable.
**MCP’s solution**: a tool needs only one MCP server implementation; any MCP-compatible AI application can use it plug-and-play. Analogy: USB-C achieved this for physical device connectivity — one interface standard that works across all devices.
**Ecosystem status (2025–2026)**: thousands of official and community MCP servers covering Slack, GitHub, Google Drive, PostgreSQL, Brave Search, Puppeteer (browser control), and other mainstream scenarios. Claude Desktop ships with built-in MCP support; users install servers locally for immediate use.
## Technical Details
MCP uses JSON-RPC 2.0 for communication, supporting two transport modes: stdio (local inter-process communication, for local tools) and HTTP+SSE (network services, for remote tools). Servers are implemented in Python or TypeScript with mature official SDKs. Security isolation: each MCP server runs in a separate process; permissions are controlled by the host application; the AI model cannot directly access the host filesystem unless explicitly authorized.
See [AI Agents Introduction](https://sunqi.org/ai-agent-introduction-en/) and the [MCP official documentation](https://modelcontextprotocol.io/).
