In one sentence: MCP (Model Context Protocol) is a unified standard for connecting AI applications to external tools and data sources. Open-sourced by Anthropic in November 2024, it is often described as "the USB-C of AI."
What problem does it solve?
Before MCP, every tool an AI app wanted to reach meant one-off custom development. Let AI query your database? Write an adapter. Let it operate GitHub? Write another one. Slack, Notion, Feishu — each required a separate integration, and switching AI apps could mean rewriting everything.
The USB-C analogy:
Keyboards used to ship with PS/2, mice with serial, printers with parallel ports — one interface per peripheral. USB defined a common standard: "as long as a device follows the USB protocol, it plugs into any computer." MCP does the same for AI agents: databases, browsers, and GitHub are the "peripherals," and they now expose "ports" built to the MCP standard, so any MCP-compatible AI app can plug in and just work.
Three-part architecture
The application running the AI — desktop AI clients, IDEs, and more. It decides "which tools to call."
The protocol connection the Host maintains with each Server — handling sessions, negotiation, and capability discovery.
The program providing the capability — the GitHub MCP, browser MCP, database MCP… one Server maps to one kind of tool or data source.
Transports
- stdio: runs locally — the AI app launches the Server process directly and talks over standard input/output (most common)
- SSE / HTTP: remote Servers accessed over the network (ideal for services deployed on machines)
The ecosystem today (August 2026)
- GitHub repos tagged with the MCP topic now exceed 15,900
- The MCP SDKs (Python + TypeScript) top 97 million downloads a month
- The official Registry lists close to 10,000 servers
- 41% of software organizations already run MCP in production
Picking one to try? See our curated server list; need to set one up? Follow the configuration tutorial.