As soon as MCP came out I thought it was over engineered crud and didn’t invest any time in it. I have yet to regret this decision. Same thing with LangChain.

This is one key difference between experienced and inexperienced devs; if something looks like crud, it probably is crud. Don’t follow or do something because it’s popular at the time.

> Centralization is Key

> (I preface that this is primarily relevant for orgs and enterprises; it really has no relevance for individual vibe-coders)

The thing about tools that "democratize" software development, whether it is Visual Studio/Delphi/QT or LLMs, is that you wind up with people in organizations building internal tools on which business processes will depend who do not understand that centralization is key. They will build these tools in ignorance of the necessity of centralization-centric approaches (APIs, MCP, etc.) and create Byzantine architectures revolving around file transfers, with increasing epicycles to try to overcome the pitfalls of such an approach.

This came up in recent discussions about the Google apps CLI that was recently released. Google initially included an MCP server but then removed it silently - and some people believe this is because of how many different things the Google Workspace CLI exposes, which would flood the context. And it seemed like in social media, suddenly a lot of people were talking about how MCP is dead.

But fundamentally that doesn’t make sense. If an AI needs to be fed instructions or schemas (context) to understand how to use something via MCP, wouldn’t it need the same things via CLI? How could it not? This article points that out, to be clear. But what I’m calling out is how simple it is to determine for yourself that this isn’t an MCP versus CLI battle. However, most people seem to be falling for this narrative just because it’s the new hot thing to claim (“MCP is dead, Long Live CLI”).

As for Google - they previously said they are going to support MCP. And they’ve rolled out that support even recently (example from a quick search: https://cloud.google.com/blog/products/ai-machine-learning/a...). But now with the Google Workspace CLI and the existence of “Gemini CLI Extensions” (https://geminicli.com/extensions/about/), it seems like they may be trying to diminish MCP and push their own CLI-centric extension strategy. The fact that Gemini CLI Extensions can also reference MCP feels a lot like Microsoft’s Embrace, Extend, Extinguish play.

If it's a remote API, I suppose the argument is that you might as well fetch the documentation from the remote server, rather than using a skill that might go out of date. You're trusting the API provider anyway.

But it's putting a lot of trust in the remote server not to prompt-inject you, perhaps accidentally. Also, what if the remote docs don't suit local conditions? You could make local edits to a skill if needed.

Better to avoid depending on a remote API when a local tool will do.

MCP is a fixed specification/protocol for AI app communication (built on top of an HTTP CRUD app). This is absolutely the right way to go for anything that wants to interoperate with an AI app.

For a long time now, SWEs seem to have bamboozled into thinkg the only way you can connect different applications together are "integrations" (tightly coupling your app into the bespoke API of another app). I'm very happy somebody finally remembered what protocols are for: reusable communications abstractions that are application-agnostic.

The point of MCP is to be a common communications language, in the same way HTTP is, FTP is, SMTP, IMAP, etc. This is absolutely necessary since you can (and will) use AI for a million different things. If you haven't yet, read the spec: https://modelcontextprotocol.io/specification/2025-11-25

MCP is fine, particular remote MCP which is the lowest friction way to get access to some hosted service with auth handled for you.

However, MCP is context bloat and not very good compared to CLIs + skills mechanically. With a CLI you get the ability to filter/pipe (regular Unix bash) without having to expand the entire tool call every single time in context.

CLIs also let you use heredoc for complex inputs that are otherwise hard to escape.

CLIs can easily generate skills from the —help output, and add agent specific instructions on top. That means you can give the agent all the instructions it needs to know how to use the tools, what tools exist, lazy loaded, and without bloating the context window with all the tools upfront (yes, I know tool search in Claude partially solves this).

CLIs also don’t have to run persistent processes like MCP but can if needed