> And the moment we, as an industry, settle on something sane, I will rip out the whole thing and adopt that, because MCP brings _nothing_ to the table that I could not do with a "proper" API using completely standard tooling.

100%. I suppose I understand MCP for user-side tooling but people seem to be reinventing the wheel because they don't understand REST. making REST requests with a well defined schema from an LLM is not all that hard.

I don't even mind it existing, it's just the way it's presented/documented/talked about like it's some special novel important concept that baffles me, and I think makes it more confusing for developer newcomers (but fine or maybe even helpful for not-particularly-technical but AI-keen/'power' users).

MCP is really a great leap because LLMs orchestrate across a collection of tools instead of running a scripted flow. The most obvious example is deep research, where the LLM sends initial queries, reads, then generates new queries and loops until it finds what it needs. This dynamic orchestration of the search tool is almost impossible to do in a scripted way. And it shows where the MCP value is - you just write simple tools, and AI handles the contextual application. You just make the backend, the front end is the LLM with human in the loop.

I made an MCP with 2 tools - generate_node and search, and with it Claude Desktop app can create a knowledge graph complete with links and everything. It scales unbounded by context size but is read/write and smarter than RAG because it uses graph structure not just embeddings. I just made the reading and writing tools, the magic of writing the nodes, linking them up, searching and analyzing them is due to AI. And again, Claude can be very efficient at wielding these tools with zero effort on my part. That is the value of MCP.

MCP is a detail here. The exact same thing would happen if generate_node and search were exposed as REST endpoints, if Claude Desktop had used REST instead of MCP.

Isn't part of the issue that LLM's are relatively bad at REST and json and all that?

I remember like 2 years ago there was big hype/revolution around ChatGPT finally being able to return ONLY valid json when you asked it to. So apparently LLMs are not that good at scripting without mistakes.

Having said that, I honestly have no clue what MCP looks like or is lol :p

MCP is a form of JSON-RPC over HTTP, so I don't think it has anything to do with that.

MCP is just json.

But to your last point: go look it up, have a read through their client/server implementations (for your language of choice). It doesn't actually take that long because the concept is actually rather simple, so I totally recommend it.

Maybe not, I think they trained the model to be especially capable of MCP tool use, and generated data this way. Other formats and systems might be slightly worse, and the whole model would probably not handle such a diversity of tools if all it had was trained on much less diverse API integrations.

Yes, I've had to deal with this at work as well. Much talk about MCP as if it's some mystical magical wonder, and not just concatenating a bunch of json to your prompt.

I personally wouldn't be surprised if the http calling abilities of LLMs end up killing MCP, or at least greatly reducing its use. If you train LLMs to start interacting with REST APIs as a great way to make them do more then a lot of the MCP use cases go away.

HATEOAS FTW

I have a tool that builds HTTP requests. LLMs can use that just fine, IF they know the API spec they should connect to. And that is the problem. If you are then required to pass the specs of dozens of APIs, you are polluting the context with too much useless info. MCP simplifies that

You also need an MCP schema to understand what the MCP server can actually do, there is no free lunch here.

yes, but in terms of token size its WAY cheaper for the AI to handle that than an OpenAPI spec

Isn't this more a limitation of the OpenAPI spec? Something like GraphQL would probably work much better

LLM already exist that capabilities since chatgpt GPT3.5 Just they don't have tools and then, tools are simply introduced by tool calling specs and it works fine already. MCP is a wrapper around that specs nothing new

You are fundamentally misunderstanding what is happening here and how this is all works.

"HTTP calling abilities of LLMs" is not some magic, new feature that is deeply integrated into LLMs. It's just a tool call like everything else - i.e. you prompt the LLM to return a JSON object that conforms to a schema.

MCP is also doing this exact same thing. It's just a wrapper protocol that tries to take care of all the details so that we don't have to deal with a million custom protocols that all accomplish the same thing but are all incompatible.

You are fundamentally misunderstanding the point I am making. LLMs have repeatedly started with training wheels and then slowly had them taken off as they have become more and more competent. MCP is another example of training wheels that will likely eventually go away. If the direct web/API calling abilities of LLMs were to improve with better trained models and some more built in support then MCP could go away and nobody would miss it.

No, you are still not getting it. MCP will never go away, or at least something like it will always end up existing.

What you are describing, "web api calling abilities were to improve" will not change anything. What sort of improvement are you thinking of? They can only get better at outputting json correctly, but that hasn't really been a problem for a long time now.

Either way, it wouldn't change anything, because MCP is a 100 other things which doesn't have anything to do with the llms using tools directly. You will never embed everything that MCP can do "into" the llm - that barely even makes sense to talk about. It's not just a wire protocol.

MCP was specifically created so LLMs can have a better understanding (better context). An app or user needs completely different endpoints than LLMs.

I mean the fact that I can bring an API spec, MCP protocol spec, and MCP SDK spec into context and say "make me an MCP server where each route equals a tool" and have that bootstrapped in <5 minutes as a starting point is pretty nice. Then you can just start chaining stuff together.

I find it kind of sad that now we finally have tooling which could make use of HATEOS[1] style REST APIs, which are technically self-documenting and in practice never really took off because you need to understand the API to write a client for it, everyone ran off and designed something else instead.

[1] https://en.wikipedia.org/wiki/HATEOAS

Looks like the GraphQL beginnings, it feels fresh now and the AI products kind of nudge everything that way but fundamentally the main point is being able to package an API into a standard format, with documented endpoints, support for authentication, and different transports.

Nothing that couldn't be solved by a well designed REST API with Swagger documentation (and authentication, and websockets), but by being the same interface for all APIs it reduces the burden on the LLM.

> but by being the same interface for all APIs it reduces the burden on the LLM

It doesn't reduce the burden for the LLM, as the LLM isn't the piece of software directly interfacing with the MCP server. It reduced the burden for people building clients that call LLMs and have to convert external interfaces into tool calls etc..

You wouldn't even need Swagger if we actually used REST. Swagger is only around because we abandoned REST for RPC.

REST is just like scrum/agile. There is apparently some sort of "standard" but really anything goes and everyone does whatever they want.

What do you mean? REST was really clearly defined in Roy Fielding's dissertation when he proposed the term in the first place.

I agree people do whatever they want and call it REST, but that's not because of a lack of definition in what their term.

I am looking into ACP and it looks something more solid compared to MCP which is like an intern project. What do you think?

> because MCP brings _nothing_ to the table that I could not do with a "proper" API using completely standard tooling.

This is what drives me crazy and has stopped my tinkering with MCP in its tracks: what is the point? It's not bringing anything new. It's usually not easier to set-up than what you're describing. Or, if you absolutely must have an LLM in the mix, normal function calling does just as well as MCP.

It's a real, "I feel like I'm taking crazy pills" moment: there's all this hype and bluster and "wow look at this", but there is no "this". Everyone's talking about "it can do" and "it'll be amazing when", but there's nothing actually built and present and ready that is impressive.

It's not really MCP that is 'this', MCP is just the glue that makes the NLP as frontend easy to conceptualize in terms of application. Everyone is getting excited because it's now relatively obvious how you can go from talking to LLM -> large orchestration happening, even though you could do this already with langchain or whatever.

MCP is basically just a protocol you can bring into the context window, do your LLM coding magic, then start up your MCP server and test your tool chain for your NLP as frontend stuff. The only difference is now it's not deterministic so stuff will get randomly fucked up.