You actually answered your own question: "If a tool genuinely has no CLI equivalent, MCP might be the right call."

I built an MCP server this week for a structured database of founder decisions extracted from podcasts (https://github.com/echomindr/echomindr). There's no CLI equivalent because the value is in the agent discovering the tool and calling it contextually, when someone asks a startup question, Claude searches real founder experiences before answering.

The key distinction: MCP makes sense for discovery and contextual tool selection. CLIs make sense when the human already knows which tool to use. For `gh pr view 123`, of course the CLI wins. But for "search my specialized dataset when relevant", that's exactly what MCP was designed for.