MCP Tool Design: Why Your AI Agent Is Failing (And How to Fix It)

The Reports of MCP's Death Have Been Greatly Exaggerated Scroll through developer forums in early 2026, and you'll find a recurring theme: MCP is dead. The takes range from dismissive ("just a fad") to resigned ("we tried it, our agents kept failing"). And the frustrations behind them are real. Teams are building MCP servers with 50+ tools, watching their agents stumble through tool selection, and concluding that the protocol itself is broken. It isn't. MCP isn't dead; it's being used poorly. And the evidence for how to use it well is now overwhelming. Over the past year, teams at GitHub, Block, and dozens of smaller shops have converged on the same set of principles. GitHub Copilot cut its tool count from 40 to 13 and saw measurable benchmark improvements. Block rebuilt its Linear MCP server three times, going from 30+ tools to just 2. The pattern is consistent: fewer tools, better descriptions, outcome-oriented design. The problem isn't the protocol. It's tool design. This article la