51–60 of 283 posts
Hmm I like the idea of providing a unified interface to all LLMs to interact with outside data. But I don't really understand why this is local only. It would be a lot more interesting if I could connect this to my github in the web app and claude automatically has access to my code repositories. I guess I can do this for my local file system now? I also wonder if I build an LLM powered app, and currently simply to R…
@jspahrsummers and I have been working on this for the last few months at Anthropic. I am happy to answer any questions people might have.
What is a practical use case for this protocol?
https://github.com/anaisbetts/mcp-youtube
Claude doesn't support YouTube summaries. I thought that was annoying! So I added it myself, instead of having to hope Anthropic would do it
Hmm I like the idea of providing a unified interface to all LLMs to interact with outside data. But I don't really understand why this is local only. It would be a lot more interesting if I could connect this to my github in the web app and claude automatically has access to my code repositories. I guess I can do this for my local file system now? I also wonder if I build an LLM powered app, and currently simply to R…
We're definitely interested in extending MCP to cover remote connections as well. Both SDKs already support an SSE transport with that in mind: https://modelcontextprotocol.io/docs/concepts/transports#ser... However, it's not quite a complete story yet. Remote connections introduce a lot more questions and complexity—related to deployment, auth, security, etc. We'll be working through these in the coming weeks, and w…
It would be a lot more interesting to write a server for this if this allowed any model to interact with my data. Everyone would benefit from having more integration and you (anthropic) still would have the advantage of basically controlling the protocol.
I don't understand the value of this abstraction. I can see the value of something like DSPy where there is some higher level abstractions in wiring together a system of llms. But this seems like an abstraction that doesn't really offer much besides "function calling but you use our python code". I see the value of language server protocol but I don't see the mapping to this piece of code. That's actually negative va…
If integrations are required to unlock value, then the platform with the most prebuilt integrations wins.
The bulk of mass adopters don't have the in-house expertise or interest in building their own. They want turnkey.
No company can build integrations, at scale, more quickly itself than an entire community.
If Anthropic creates an integration standard and gets adoption, then it either at best has a competitive advantage (first mover and ownership of the standard) or at worst prevents OpenAI et al. from doing the same to it.
(Also, the integration piece is the necessary but least interesting component of the entire system. Way better to commodify it via standard and remove it as a blocker to adoption)
Earlier quoted context omitted.
It's not about the interface to make a request to a server, it's about how the client and server can interact. For example: When and how should notifications be sent and how should they be handled? --- It's a lot more like LSP.
Nobody [who knows what they're doing] wants their LLM API layer controlling anything about how their clients and servers interact though.
I don't understand the value of this abstraction. I can see the value of something like DSPy where there is some higher level abstractions in wiring together a system of llms. But this seems like an abstraction that doesn't really offer much besides "function calling but you use our python code". I see the value of language server protocol but I don't see the mapping to this piece of code. That's actually negative va…
The secret sauce part is the useful part -- the local vector store. Anthropic is probably not going to release that without competitive pressure. Meanwhile this helps Anthropic build an ecosystem. When you think about it, function calling needs its own local state (embedded db) to scale efficiently on larger contexts. I'd like to see all this become open source / standardized.
So does the entire db get fed into the context? Or is there another layer in between. What if the database is huge, and you want to ask the AI for the most expensive or best selling items? With RAG that was only vaguely possible and didnt work very well.
Sorry I am a bit new but trying to learn more.
The Zed editor team collaborated with Anthropic on this, so you can try features of this in Zed as of today: https://zed.dev/blog/mcp
I don't understand the value of this abstraction. I can see the value of something like DSPy where there is some higher level abstractions in wiring together a system of llms. But this seems like an abstraction that doesn't really offer much besides "function calling but you use our python code". I see the value of language server protocol but I don't see the mapping to this piece of code. That's actually negative va…