Le problème : les intégrations M par N

A chat model alone only knows text. To be useful, it needs to reach your files, databases, and apps. That reaching-out is an integration.

Before MCP, each AI app wrote its own private glue code for every tool. A connector built for one app could not be reused by another. 🔌

Picture M AI apps on one side and N tools on the other. Each app must learn to talk to each tool, all by itself.

Wiring every app to every tool means up to M times N connectors. With 10 apps and 10 tools, that is already 100 pieces to build and maintain.

Ten different teams each rewrite a connector for the same database. That is the same work repeated ten times, with ten chances to get it wrong.

When a tool changes its API, every custom connector that touched it can break at once. Maintenance becomes a never-ending chase.

This is the same mess old hardware had: every device needed its own special cable. The pain is real, and the pattern is well known.

A shared standard flips the math. Build each tool once to one spec, and any app that speaks it can connect for free.

With a common protocol you go from M times N down to M plus N. Ten apps plus ten tools is just twenty pieces, not a hundred.

The Model Context Protocol is that shared standard for AI. It defines one common way for models and tools to talk to each other.

Build one MCP server for your tool, and every MCP-aware app can use it. Your integration is written once and reused everywhere. ✨

Think about how the connector count changes with a shared standard.

Custom glue made integrations explode to M times N. A shared standard like MCP brings it down to M plus N, so each tool is built once and reused.