What problem does the Model Context Protocol solve?

Tool use works, but it created a mess. Every model had its own way of describing tools, and every tool needed wiring for every model. With M models and N tools you face roughly M times N custom integrations, each one fragile and bespoke: a Slack integration written for one model’s tool-calling format has to be rewritten, not just reconfigured, to work with the next.

The Model Context Protocol, introduced by Anthropic in late 2024, is an open standard that collapses that.^[1] Instead of a custom connector per pair, a tool exposes itself once over MCP, and any MCP-aware model can use it. The official documentation frames MCP as a standard way to connect AI applications to external systems.^[2] The common shorthand is that MCP is a USB-C port for AI: one plug, not a drawer of cables.

Fewer integrations is the visible change. The real one is who does the work. Before MCP, connecting model M to tool N was a joint problem: someone had to know both the model’s tool-calling format and the tool’s API. After MCP, the tool author writes one server, once, without needing to know which models will eventually call it; the model vendor implements one client, once, without needing to know which tools users will connect. Neither side of the M × N grid has to coordinate with the other again.

Most MCP servers expose a single tool or data source: a filesystem, a database, a SaaS product’s API. Some do more. Physea’s Liminality MCP takes a whole question, breaks it into sub-questions, ties each to a real source, and hands back a worked answer you can check — one server exposing a reasoning process, not just a lookup. See MCP architecture for how a server actually declares what it offers.