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.
Built Echomindr this week — extracted 1,150 structured decisions, lessons, and signals from 96 podcast episodes (HIBT, Lenny's, Acquired, YC, 20VC) and made them searchable via API and MCP server.
The idea: AI agents give generic startup advice. This gives them access to what founders actually did, with verbatim quotes and timestamp links to the source.
Stack: Deepgram + Claude + SQLite + FastAPI. Total cost under €50.
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.