Imagine you are building an AI-powered IDE. You want it to read GitHub, query Postgres, search internal docs, run the terminal, and post to Slack. That is five tools. You build them, you ship.
Now your competitor wants the same five tools. So does the support tool, the analytics dashboard, the customer-success co-pilot, and the mobile assistant. Five products times five tools is not 25 integrations; it is 25 different implementations of the same five integrations, each maintained separately, each breaking in its own unique way when an upstream API changes. That is the N×M problem: N AI products multiplied by M tools yields N·M custom bridges.
The cost is not theoretical. Real teams spend more time on glue code than on the features users see. Each upstream change — a Postgres role permission tweak, a Slack scope rename — triggers a fan-out of brittle updates across every product. Users get inconsistent behavior across tools that ought to feel identical (because the bridges interpret the upstream slightly differently).
The MCP answer is the same answer every successful protocol gives: collapse N·M into N+M. Each AI product implements one MCP client; each tool publishes one MCP server. A new product gets all existing tools for free; a new tool reaches every product for free. The protocol is the contract that makes the math add instead of multiply.