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When does MCP make sense vs CLI?

ejholmes.github.io

201–210 of 301 posts

Re: When does MCP make sense vs CLI?

#201
This is the dumbest most myopic take I’ve seen in awhile. MCP servers aren’t just used for geeks using Claude Code. If I have an app in production that uses an MCP server what is he suggesting? That my LLM create a shell script and run it inside of a Docker container?

Re: When does MCP make sense vs CLI?

#202

> I tried to avoid writing this for a long time, but I'm convinced MCP provides no real-world benefit IMO this is 100% correct and I'm glad someone finally said it. I run AI agents that control my entire dev workflow through shell commands and they are shockingly good at it. the agent figures out CLI flags it has never seen before just from --help output. meanwhile every MCP server i've used has been a flaky process…

While I do agree that MCP was probably bit too far from whats required, there is some benefit for sure. Providing information in a consistent format across all the services makes it easier work with. It lowers the brittleness of figuring out things making the products built using LLMs more stable/predictable. Most importantly it becomes the latest version of the documentation about a service. This can go a long way in M2M communication, pretty much standardization of Application layer.

Oh wait, things like open-api and all already exists and pretty much built to solve the same problem.

Re: When does MCP make sense vs CLI?

#205
It seems that the author thinks that AI use is limited to developers, I don't understand how short slighted is the debate between CLI and MCP. Sure, CLI are more convenient, but currently most AI users consume LLM through online tools like ChatGPT

A basic example: a company using ChatGPT or Claude, and wanting to connect their business tools (ex: marketing, sales, project management...). in that case MCP is perfect from an enterprise point of view, and the integration can be managed at the company level.

Re: When does MCP make sense vs CLI?

#206

Earlier quoted context omitted.

Could you expand on this some more? I'm not quite following. I agree with the sandboxing challenge of a CLI, although I think any CLI (or MCP) wrapping an http API should be subject to a sane permissioning system that's a first class concept in the API itself. That's in my opinion the correct way to limit what different users/tools/agents can do. But I don't fully understand the Streamable HTTP point.

I doesn't matter how it "should" work. In the real world you need to interact with external systems which don't have granular enough permission schemes. People out here letting Claude code run CLIs using their own user permissions are morons waiting to have their data deleted.

I get that. Should and DO are different. But you aren't addressing my Streamable HTTP question which is the heart of what I asked.

Re: When does MCP make sense vs CLI?

#208
post #91

Earlier quoted context omitted.

CLI tools are designed to provide complete documentation using —help. Given LLMs are capable of fully understanding the output then how is the MCP standardization any better than the CLI —help standardization?

Read only access control.

Instead of giving them read only credentials let’s spin up a server that wraps the cli and only has read only credentials…

Seems pretty roundabout.

Re: When does MCP make sense vs CLI?

#210

Earlier quoted context omitted.

I would spend less time with theory and more time with practice to understand what people are getting at. MCP and CLI could, in theory, be the same. But in practice as it stands today, they are not. > MCP is just a way to write this in a structured way, Nope! You are not understanding or are actively ignoring the difference which has been explained by 20+ comments just here. It's not a controversial claim, it's a mut…

Fair enough, I’ll give it a try when I’ll have time for it. I recognize that MCP as typically used would eat a good chunk of context - shoving all those API specs is wasteful for sure. The solution to this, I believe, is either RAG or single-tool (Context7-like), where relevant APIs are only provided on demand from models’ intermediate requests. Caveat is model may need training to use that efficiently (or even at al…

I'm rooting for you, to be clear! It sounds like your approach is more sophisticated than the average, and this is a pain point that is starting to get a lot of attention.
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