Quandri Engineering argues that MCP (Model Context Protocol) creates more problems than it solves for daily development work. While promoted as 'the USB-C of the AI ecosystem,' the protocol forces LLMs to carry unnecessary overhead through bloated context windows and unreliable server processes.

The context window issue is particularly severe. With four MCP servers connected, tool definitions consume 10.5% of Claude's 200K token context window and 16.5% of GPT-4o's 128K limit. Linear alone accounts for over 12,800 tokens through its 42 tool definitions, even when developers only need basic functions like get_issue and save_issue.

Reliability suffers from architectural overhead. MCP servers run as separate processes that fail mid-session, require constant re-authentication, and create 3x slower response times compared to direct API calls. The protocol also duplicates existing CLI and API functionality that developers already understand, adding complexity without clear benefits.

Quandri recommends a CLI-first approach using Skills patterns that load tool definitions only when needed. They use native CLIs for everyday tools like gh and psql, Skills for repeatable workflows, and reserve MCP for services lacking strong CLI options. For most developer workflows, MCP represents unnecessary abstraction.