The Integration Problem
Most AI coding tools need to talk to external systems. Your agent needs to read GitHub PRs, query a database, check documentation, or automate a browser. Before MCP, each integration was a custom job — every combination of AI application and external tool required its own connector. With M different AI apps and N different tools, you end up building M×N integrations. That doesn't scale.
The Model Context Protocol solves this by defining a single, open standard for how AI applications connect to external systems. Build an MCP server once for your tool, and every MCP-compatible AI client can use it. Build an MCP client once in your AI app, and it can connect to any MCP server. The M×N problem becomes M+N.
Anthropic introduced MCP in November 2024. [1] By mid-2026, it has become the de facto standard for AI tool integration, with thousands of community-built servers, [2] Tier 1 SDKs surpassing one billion total downloads in both TypeScript and Python, [3] and adoption across Claude, ChatGPT, Cursor, and dozens of other AI applications.
How MCP Works
MCP uses a client-server architecture built on JSON-RPC messages. The moving parts are straightforward:
Hosts are the AI applications you interact with — Claude Code, Cursor, an IDE plugin. A host contains one or more MCP clients.
Clients live inside hosts and maintain connections to MCP servers. They handle the protocol-level communication — sending requests, receiving responses, managing the session lifecycle.
Servers are lightweight programs that expose capabilities to clients. A GitHub MCP server exposes repository operations. A PostgreSQL MCP server exposes database queries. A Playwright MCP server exposes browser automation. Each server is focused and composable — you run the ones you need.
The protocol defines three primitives that servers can expose:
Tools are actions the AI can invoke — run a query, create a PR, click a button in a browser. The AI model decides when and how to call them based on the conversation context.
Resources are data the AI can read — file contents, database schemas, API documentation. Resources provide context without requiring the AI to take an action.
Prompts are reusable templates that guide the AI's behavior for specific tasks — a code review template, a migration checklist, a debugging workflow.