The Model Context Protocol (MCP), Explained
MCP is becoming the USB-C of AI tools — one open standard to connect models to your data and systems. Here's how it works.
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic, for connecting AI models to external tools, data and systems. Think of it as a universal adapter: build one MCP server for a capability, and any MCP-compatible AI — like Claude — can use it.
Servers and clients
An MCP server exposes resources and tools — a database, an API, a set of actions — in a standard, discoverable way. An MCP client lives inside an AI application or agent and connects to those servers, letting the model discover and call the tools it needs.
Why it matters
Before MCP, every AI-to-tool integration was bespoke glue code. MCP standardizes discovery and invocation, so your capabilities become reusable across every agent and client. That reduces maintenance, avoids lock-in, and future-proofs your AI stack as the ecosystem converges on the protocol.
- Build once, use across every MCP-compatible agent.
- Add auth, scoped permissions and rate limits for safe access.
- Deploy locally or as a remote hosted server.