[gyre007]

Something is telling me this _might_ turn out to be a huge deal; I can't quite put a finger on what is that makes me feel that, but opening private data and tools via an open protocol to AI apps just feels like a game changer.

[TeMPOraL]

It's just function calling with a new name and a big push from the LLM provider, but this time it's in the right direction. Contrast with OpenAI's "GPTs", which are just function calling by another name, but pushed in the wrong direction - towards creating a "marketplace" controlled by OpenAI.

I'd say that thing you're feeling comes from witnessing an LLM vendor, for the first time in history, actually being serious about function calling and actually wanting people to use it.

[jjfoooo4]

But either way the interface is just providing a json schema of functions along with your chat completion request, and a server with ability to parse and execute the response. I’m not really seeing where a new layer of abstraction helps here (much less a new “protocol”, as though we need a new transport layer?

It smells like the thinking is that you (the developer) can grab from a collection of very broad data connectors, and the agent will be able to figure out what to do with them without much custom logic in between. Maybe I’m missing something

[TeMPOraL]

> It smells like the thinking is that you (the developer) can grab from a collection of very broad data connectors, and the agent will be able to figure out what to do with them without much custom logic in between.

This has always been the idea behind tools/function calling in LLMs.

What MCP tries to solve is the NxM problem - every LLM vendor has their own slightly different protocols for specifying and calling tools, and every tool supplier has to handle at least one of them, likely with custom code. MCP aims to eliminate custom logic at the protocol level.

[MattDaEskimo]

LLMs can potentially query _something_ and receive a concise, high-signal response to facilitate communications with the endpoint, similar to API documentation for us but more programmatic.

This is huge, as long as there's a single standard and other LLM providers don't try to release their own protocol. Which, historically speaking, is definitely going to happen.

[gyre007]

> This is huge, as long as there's a single standard and other LLM providers don't try to release their own protocol

Yes, very much this; I'm mildly worried because the competition in this space is huge and there is no shortage of money and crazy people who could go against this.

[bloomingkales]

They will go against this. I don’t want to be that guy, but this moment in time is literally the opening scene of a movie where everyone agrees to work together in the bandit group.

But, it’s a bandit group.

[defnotai]

Not necessarily. There’s huge demand to simplify the integration process between frontier models and consumers. If specs like this wind up saving companies weeks or months of developer time, then the MCP-compatible models are going to win over the more complex alternatives. This unlocks value for the community, and therefore the AI companies

[csomar]

One of the biggest issues of LLM is that they have a lossy memory. Say there is a function from_json that accepts 4 arguments. An LLM might predict that it accepts 3 arguments and thus produce non-functional code. However, if you add the docs for the function, the LLM will write correct code.

With the LLM being able to tap up to date context (like LSP), you won't need that back-and-forth dance. This will massively improve code generations.

[orliesaurus]

This is definitely a huge deal - as long as there's a good developer experience - which IMHO we're not there yet!

[somnium_sn]

Any feedback on developer experience is always welcomed (preferably in github discussion/issue form). It's the first day in the open. We have a long long way to go and much ground to cover.