What MCP is and the problem it solves

Every time you connect an AI model to a tool — a database, an API, a file system — you write glue code. Do that for M models and N tools and you have M×N integrations to build and maintain. The Model Context Protocol (MCP) exists to collapse that into M+N: each model speaks MCP, each tool exposes MCP, and they connect through one standard interface. People call it "USB-C for AI tools," and it's an apt analogy.

MCP is an open protocol, introduced by Anthropic in late 2024 and now maintained as an open standard with its own governance, working groups, and multi-language SDKs. It's built on JSON-RPC 2.0, so at the wire level it's just structured request/response messages.

What MCP standardizes is the contract between an AI application and an external capability: how the application discovers what a tool offers, how it calls it, and how results come back. Before MCP, every integration invented its own answer to those questions. After MCP, a server you build works with any MCP-compatible client — Claude Desktop, Claude Code, VS Code, and a growing list of others — without per-client code.

A currency warning up front, because it will save you real pain. MCP shipped a major revision, spec version 2026-07-28, that reworked large parts of the 2025-era protocol. Most blog posts, videos, and even some official-looking tutorials still teach the old shape. This course teaches the current spec: a stateless protocol, the MCPServer class in the Python SDK v2, Streamable HTTP transport, and elicitation as the primary way to ask the user for input. If you see a tutorial leaning on an initialize handshake, FastMCP from the official package, SSE as a transport, or sampling/roots, it's showing you the old version.

The payoff for learning MCP: you build an integration once and it works everywhere, and you tap into an ecosystem of existing servers rather than reinventing each one.