Why MCP Exists: Solving the M×N Integration Problem for AI

Imagine 10 AI assistants and 50 data sources (databases, APIs, file systems, SaaS tools). Without a standard, each AI vendor writes 50 integrations; each tool vendor writes 10. That's 500 connection points, all custom. When one side changes its API, the other side breaks. MCP changes the math: each AI implements one MCP client; each tool publishes one MCP server. 10 + 50 = 60 connection points total.

Anthropic could have built a proprietary plugin system — like OpenAI did with ChatGPT plugins. Instead, they published an open specification and reference SDKs under a permissive license. The reasoning: a proprietary standard locks users in but produces a smaller ecosystem. An open standard creates a larger ecosystem that benefits everyone, including Anthropic's own products.

The rise of coding-focused AI clients (Cursor, Windsurf, Cline) created a market for exactly this: developers who want their AI to access real systems, not just generate text about them. MCP arrived at the moment the demand was largest.

Any MCP server you set up today works with any MCP-compatible client you use tomorrow. Your investment in configuration is portable. If you switch from Claude Desktop to Cursor, your servers move with you.