In short
MCP (Model Context Protocol) is an open standard that lets AI models connect to external tools, data sources and services through one common interface, instead of every app inventing its own integration. It is often described as a USB-C port for AI: any MCP-compatible model can plug into any MCP server and immediately use what it offers. An MCP server exposes three kinds of capability - tools (actions the model can run), resources (read-only data it can fetch) and prompts (reusable templates) - so a model can do work in the real world without custom glue code for each connection.
Why MCP exists
Before MCP, connecting a model to a database, a file system or an API meant writing bespoke integration code for every pairing, which did not scale. MCP standardises that contract once: write an MCP server for a system and any MCP-aware client (Claude Code, an IDE, a chat app) can use it. Introduced by Anthropic in late 2024, it has become the de facto standard, with official SDKs for TypeScript, Python, C#, Java and Swift and hundreds of public servers by 2026.
Tools, resources and prompts
An MCP server can expose any mix of three primitives, each with a standard way to list and use it.
- Tools: actions the model can call, like "run a query" or "create a file". This is tool calling over a shared protocol.
- Resources: read-only data the model can fetch, like a document, a database row or a log.
- Prompts: reusable prompt templates a server offers so clients get a consistent starting point.
How a connection works
A host application (the AI app) runs one or more clients, and each client holds a dedicated connection to a single MCP server. When the model needs to act, it asks the client to call a tool on the server; the server does the real work (say, runs a SQL query) and returns a structured result the model can read. The model never touches the database directly, which keeps permissions and security in the server where you control them.