What is Model Context Protocol (MCP)?
An open standard that lets AI models connect to external tools, data sources, and services through a unified interface.
Definition
Model Context Protocol (MCP) is an open protocol published by Anthropic in late 2024 that standardizes how AI models connect to external context sources. Before MCP, every AI integration required custom glue code. MCP defines a common interface: an MCP server exposes tools and resources; an MCP client (like Claude Code or Claude Desktop) calls them. This means any AI application can plug into any MCP-compatible data source, tool, or service without custom integration work.
Why it matters
MCP is doing for AI integrations what USB did for hardware peripherals — standardizing the connection so you don't need a different cable for every device. In 2025, MCP adoption accelerated rapidly: hundreds of MCP servers were published for databases, GitHub, Notion, Slack, browser automation, and more. AI engineers and advanced power users who understand MCP can connect AI to any system far faster than those building custom integrations.
How it works
An MCP server is a small process that exposes a set of tools (functions the AI can call) and resources (data the AI can read). The MCP client connects to the server via a standard protocol (JSON-RPC over stdio or HTTP). When the AI needs to use a tool or access data, it calls the MCP server; the server executes the action and returns the result. Claude Code, Claude Desktop, and Cursor all support MCP natively.
Examples in practice
Claude Code with a GitHub MCP server
By connecting a GitHub MCP server to Claude Code, the AI can read issues, create pull requests, review diffs, and comment on code directly from the terminal — no manual copy-pasting.
Claude Desktop with a database MCP server
Connect an MCP server to your Postgres database and Claude Desktop can query it in natural language, generate reports, and identify anomalies — without you writing any SQL.
Custom MCP servers for internal tools
Companies build MCP servers for their internal APIs so AI assistants can access proprietary data (CRM records, inventory, analytics) through a secure, standardized interface.