Why replace it at all? Just remove it. I use AI every day and don't use MCP. I've built LLM powered tools that are used daily and don't use MCP. What is the point of this thing in the first place?

It's just a complex abstraction over a fundamentally trivial concept. The only issue it solves is if you want to bring your own tools to an existing chatbot. But I've not had that problem yet.

▲

anon84873628 12 hours ago | parent | next [-]

Ah, so the "I haven't needed it so it must be useless" argument.

There is huge value in having vendors standardize and simplifying their APIs instead of having agent users fix each one individually.

▲

ianbutler 11 hours ago | parent | next [-]

Possible legit alternative:

Have the agents write code to use APIs? Code based tool calling has literally become a first party way to do tool calling.

We have a bunch of code accessible endpoints and tools with years of authentication handling etc built in.

https://www.anthropic.com/engineering/advanced-tool-use#:~:t...

Feels like this obviates the need for MCP if this is becoming common.

▲

anon84873628 11 hours ago | parent [-]

That solution will not work as well when the interfaces have not been standardized in a way that makes it so easy to import them into a script as a library.

Coding against every subtly different REST API is as annoying with agents as it is for humans. And it is good to force vendors to define which parts of the interface are actually important and clean them up. Or provide higher level tasks. Why would we ask every client to repeat that work?

There are also plenty of environments where having agents dynamically write and execute scripts is neither prudent nor efficient. Local MCP servers strike a governance balance in that scenario, and remote ones eliminate the need entirely.

▲

simianwords 7 hours ago | parent [-]

I don’t agree on the first part. What sort of llm can’t understand a swagger spec? Why do you think it can’t understand this but can understand mcp?

On runtime problems yes maybe we need standardisation.

▲

anon84873628 5 hours ago | parent [-]

Well if everyone was already using Swagger then yes it would be a moot point. It seems you do in fact agree that the standardized manifest is important.

	▲	simianwords 2 hours ago \| parent [-]
		Wait why do you assume any standardisation is required? Just put the spec whether swagger or not

▲

ModernMech 5 hours ago | parent | prev | next [-]

I thought the whole point of AI was that we wouldn't have to do these things anymore. If we're replacing engineering practice with different yet still basically the same engineering practice, then AI doesn't buy us much. If AI lives up to their marketing hype, then we shouldn't need MCP.

	▲	anon84873628 5 hours ago \| parent [-]
		Hm. Well maybe you are mistaken and that dichotomy is false.

▲

otabdeveloper4 3 hours ago | parent | prev [-]

> There is huge value in having vendors standardize and simplifying their APIs

Yes, and it's called OpenAPI.

▲

maxwellg 13 hours ago | parent | prev | next [-]

> The only issue it solves is if you want to bring your own tools to an existing chatbot.

That's a phenomenally important problem to solve for Anthropic, OpenAI, Google, and anyone else who wants to build generalized chatbots or assistants for mass consumer adoption. As well as any existing company or brand that owns data assets and wants to participate as an MCP Server. It's a chatbot app store standard. That's a huge market.

▲

p_ing 15 hours ago | parent | prev | next [-]

> What is the point of this thing in the first place?

It's easier for end users to wire up than to try to wire up individual APIs.

▲

tunesmith 13 hours ago | parent | prev | next [-]

So, I've been playing with an mcp server of my own... the api the mcp talks to is something that can create/edit/delete argument structures, like argument graphs - premises, lemmas, and conclusions. The server has a good syntactical understanding of arguments, how to structure syllogisms etc.

But it doesn't have a semantic understanding because it's not an llm.

So connecting an llm with my api via MCP means that I can do things like "can you semantically analyze the argument?" and "can you create any counterpoints you think make sense?" and "I don't think premise P12 is essential for lemma L23, can you remove it?" And it will, and I can watch it on my frontend to see how the argument evolves.

So in that sense - combining semantic understanding with tool use to do something that neither can do alone - I find it very valuable. However, if your point is that something other than MCP can do the same thing, I could probably accept that too (especially if you suggested what that could be :) ). I've considered just having my backend use an api key to call models but it's sort of a different pattern that would require me to write a whole lot more code (and pay more money).

▲

thomasfromcdnjs 12 hours ago | parent | prev | next [-]

I have Linear(mcp) connected to ChatGPT and my Claude Desktop, and I use it daily from both.

For the MCP nay sayers, if I want to connect things like Linear or any service out there to third party agentic platforms (chatgpt, claude desktop), what exactly are you counter proposing?

(I also hate MCP but gets a bit tiresome seeing these conversations without anyone addressing the use case above which is 99% of the use case, consumers)

▲

theturtletalks 12 hours ago | parent [-]

Easy. Just tell the LLM to use the Linear CLI or hit their API directly. I’m only half-joking. Older models were terrible at doing that reliably, which is exactly why we created MCP.

Our SaaS has a built-in AI assistant that only performs actions for the user through our GraphQL API. We wrapped the API in simple MCP tools that give the model clean introspection and let us inject the user’s authenticated session cookie directly. The LLM never deals with login, tokens, or permissions. It can just act with the full rights of the logged-in user.

MCP still has value today, especially with models that can easily call tools but can’t stick to prompt. From what I’ve seen in Claude’s roadmap, the future may shift toward loading “skills” that describe exactly how to call a GraphQL API (in my case), then letting the model write the code itself. That sounds good on paper, but an LLM generating and running API code on the fly is less consistent and more error-prone than calling pre-built tools.

▲

Yeroc 8 hours ago | parent [-]

Easy if you ignore the security aspects. You want to hand over your tokens to your LLM so it can script up a tool that can access it? The value I see in MCP is that you can give an LLM access to services via socket without giving it access to the tokens/credentials required to access said service. It provides at least one level of security that way.

	▲	DANmode 5 hours ago \| parent [-]
		The point of the example seemed to be connecting easily to a scoped GraphQL API.

▲

tonmoy 6 hours ago | parent | prev | next [-]

The less context switching LLMs of current day need to do the better they seem to perform. If I’m writing C code using an agent but my spec needs complex SQL to be retried then it’s better to give access to the spec database through MCP to prevent the LLM from going haywire

▲

nextaccountic 2 hours ago | parent | prev | next [-]

How do I integrate tool calling in an IDE (such as Zed) without MCP?

▲

UncleEntity 15 hours ago | parent | prev [-]

Isn't that the way if works, everybody throws their ideas against the wall and sees what sticks? I haven't really seen anyone recommend using xml in a long while...

And isn't this a 'remote' tool protocol? I mean, I've been plugging away at a VM with Claude for a bit and as soon as the repl worked it started using that to debug issues instead of "spray and pray debugging" or, my personal favorite, make the failing tests match the buggy code instead of fixing the code and keeping the correct tests.