MCP Is Dead: Why Model Context Protocol Falls Short

The USB-C Dream That Wasn't

MCP was supposed to be 'the USB-C of the AI ecosystem'—a universal connector between any AI model and any tool. Quandri's engineering team, after using MCP daily in production, found the promise doesn't hold up under real-world conditions.

Problem 1: Context Window Waste

Connecting four MCP servers burns through roughly 10.5% of Claude's 200K context window on tool definitions alone—before any actual work begins. Linear alone contributes over 12,800 tokens across 42 tool definitions. The team measured that MCP consumes approximately 65x more tokens than the equivalent CLI approach for an identical task.

Problem 2: Operational Reliability

Initialization failures, authentication problems, response latency 3x slower than direct API calls, mid-session crashes, and unclear permission scoping are all recurring issues. What should be transparent infrastructure becomes a debugging burden.

Problem 3: Redundancy

MCP largely duplicates functionality already available through CLIs and APIs. CLI tools offer superior composability and debuggability—you can inspect output in the terminal, chain commands, and integrate with existing workflows without additional abstraction layers.

When MCP Still Makes Sense

The article doesn't advocate abandoning MCP entirely. Three scenarios where it remains the right tool: web-only services without CLI access, products designed for non-developer users, and applications requiring real-time bidirectional communication. For standard development workflows, lightweight CLI integration and context-efficient skills patterns are the recommended alternative.