MCP Isn’t Dead. You just have no Skills!

MCP Isn’t Dead. You’re Just Deploying It Wrong.

If you’ve been on AI developer forums lately you’ve seen the same hot take a hundred times: MCP is dead. Eric Holmes wrote “MCP is dead. Long live the CLI” and it hit the top of Hacker News in February. A couple weeks later Garry Tan, the CEO of Y Combinator, posted on X that “MCP sucks honestly,” pointing to context window consumption and bad auth. And there’s a 2025 research paper, MCPGAUGE, that put real numbers on the problem — across six commercial LLMs they measured a 9.5% average drop in accuracy and input token volume inflating by anywhere from 3.25x to 236.5x.

Read those three together and “MCP is dead” looks airtight. But the deployments most of these critiques are pointing were never built to succeed in the first place. Most teams hook up four or five MCP servers, dump every tool into the model’s context, write zero guidance, and then blame the protocol when the assistant hallucinates a column name or invents an asset count.

That’s not an MCP failure. That’s a deployment failure. The protocol is fine. The shape of the failure tells you the developer never gave the model what it actually needed: structure, constraints, and the discipline to look before it speaks.