The Model Context Protocol (MCP) is an open standard introduced by Anthropic in late 2024 to solve a problem every AI developer has hit: connecting AI assistants to the tools and data they actually need to be useful. Before MCP, every integration was a one-off — a custom API client, a bespoke prompt, a pile of glue code that broke the moment a vendor shipped an update.

The Problem MCP Solves

Think about how a smart assistant should work. It needs to read your files, query your database, search your docs, send messages on your behalf. Each of those is a different service with a different API. Traditionally that meant M×N integrations — every assistant multiplied by every tool. MCP collapses that to M+N.

How It Works

MCP is built on a clean client–server model with JSON-RPC 2.0 as the wire format:

  • Host — the AI application (a desktop app, an IDE, a CLI) that wants to use tools.
  • Client — the connector inside the host that maintains a 1:1 session with a server.
  • Server — a lightweight program exposing tools, resources, and prompts.

A conversation typically flows like this: the host asks the server what it can do (tools/list), the model picks a tool, the host invokes it (tools/call), and the result comes back into the model’s context. Clean, discoverable, and — crucially — versioned.

Transports

MCP supports two main transports. stdio runs the server as a subprocess (great for local tools), while Streamable HTTP and SSE let servers live remotely behind an API gateway. This is why a single protocol can power both a local file-system tool and a hosted SaaS connector.

Why It Matters

The real power is the network effect of context. Once you build an MCP server for your product, every MCP-compatible assistant can use it — Claude, WorkBuddy, Cursor, and a growing list. You write the integration once, and the entire ecosystem benefits. That’s the “USB-C of AI” analogy: one standard, many devices.

Getting Started

The fastest way to understand MCP is to stand up a tiny server. A minimal SSE server in Node needs only a few dozen lines: expose an /sse endpoint, respond to initialize, and implement tools/list and tools/call. In about an afternoon you can wire a real capability into an AI assistant.

If you’re building anything that an AI might want to touch — a CMS, a database, an internal API — an MCP server is the highest-leverage integration you can ship right now.