The Model Context Protocol (MCP) was supposed to be the great unifier — a standard way for AI agents to talk to the tools and services they need to get work done. Launched in late 2024, it was quickly anointed "the USB-C of the AI ecosystem," adopted by Anthropic, OpenAI, and a growing ecosystem of tool providers.
But the honeymoon may be over. A devastating new analysis from Quandri Engineering, combined with a heated Hacker News debate (195 points, 174 comments), has put a serious dent in MCP's reputation. The verdict? MCP eats context, has low reliability, and overlaps significantly with existing CLI and API tools that already work perfectly well.
Problem 1: It Devours the Context Window
The most damning finding from Quandri's analysis is the sheer volume of tokens consumed by MCP tool definitions. In their real-world stack (Linear, Notion, Slack, and Postgres MCP servers), tool definitions alone consumed over 21,000 tokens — that's 10.5% of a Claude 200K context window, and 16.5% of GPT-4o's 128K context.
MCP Server
