Oh you're misunderstanding MCP here.

MCP was created so llm companies can have a plugin system. So instead of them being the API provider, they can become the platform that we build apps/plugins for, and they become the user interface to end consumers.

This is incredibly simple and neat! Love it!

Will have a think about how this can extended to other types of uses.

I have personally been trying to replace all tools/MCPs with a single “write code” tool which is a bit harder to get to work reliably in large projects.

Mario has some fantastic content, and has really shaped how I think about my interface to coding tools. I use a modified version of his LLM-as-crappy-state-machine model (https://github.com/badlogic/claude-commands) for nearly all my coding work now. It seems pretty clear these days that progressive discovery is the way forward (e.g. skills), and using CLI tools rather than MCP really facilitates that. I've gone pretty far down the road of writing complex LLM tooling, and the more I do that the more the simplicity and composability is appealing. He has a coding agent designed along the same principles, which I'm planning to try out (https://github.com/badlogic/pi-mono/tree/main/packages/codin...).

Moderne Ai agent tool have have a setting where you can trimm down the numbers of tools from an MCP server. Usefull to avoid overwhelming the LLM with 80 tools description when you only need 1

Yeah, I'm still confused as to why so many people in "AI engineering" seem to think that MCPs are the key to everything.

They are great if you have a UI that you want and it needs a plugin system, obviously.

But the benefits become much more marginal for a developer of enterprise AI systems with predefined tool selections. They are actually getting overused in this space, if anything, sometimes with security as a primary casualty.