161–170 of 283 posts
This is great but will be DOA if OpenAI (80% market share) decides to support something else. The industry trend is that everything seems to converge to OpenAI API standard (see also the recent Gemini SDK support for OpenAI API).
where do you get that number?
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…
> 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.
In which case the "API" would be governed by a contract between Anthropic and Github, to which you're a third party (read: sharecropper).
Interoperability on the web has already been mostly killed by the practice of companies integrating with other companies via back-channel deals. You are either a commercial partner, or you're out of the playground and no toys for you. Them starting locally means they're at least reversing this trend a bit by setting a different default: LLMs are fine to integrate with arbitrary code the user runs on their machine. No need to sign an extra contact with anyone!
@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.
1. The sampling documentation is confusing. "Sampling" means something very specific in statistics, and I'm struggling to see any connection between the term's typical usage and the usage here. Perhaps "prompt delegation" would be a more obvious term to use.
Another thing that's confusing about the sampling concept is that it's initiated by a server instead of a client, a reversal of how client/server interactions normally work. Without concrete examples, it's not obvious why or how a server might trigger such an exchange.
2. Some information on how resources are used would be helpful. How do resources get pulled into the context for queries? How are clients supposed to determine which resources are relevant? If the intention is that clients are to use resource descriptions to determine which to integrate into prompts, then that purpose should be more explicit.
Perhaps a bigger problem is that I don't see how clients are to take a resource's content into account when analyzing its relevance. Is this framework intentionally moving away from the practice of comparing content and query embeddings? Or is this expected to be done by indices maintained on the client?
Earlier quoted context omitted.
What is a practical use case for this protocol?
A few common use cases that I've been using is connecting a development database in a local docker container to Claude Desktop or any other MCP Client (e.g. an IDE assistant panel). I visualized the database layout in Claude Desktop and then create a Django ORM layer in my editor (which has MCP integration). Internally we have seen people experiment with a wide variety of different integrations from reading data file…
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.
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.
I'm wondering if there will be anything that's actually LLM-specific about these API's. Are they useful for ordinary API integration between websites?
In the "Protocol Handshake" section of what's happening under the hood - it would be great to have more info on what's actually happening.
For example, more details on what's actually happening to translate the natural language to a DB query. How much config do I need to do for this to work? What if the queries it makes are inefficient/wrong and my database gets hammered - can I customise them? How do I ensure sensitive data isn't returned in a query?
Awesome! In the "Protocol Handshake" section of what's happening under the hood - it would be great to have more info on what's actually happening. For example, more details on what's actually happening to translate the natural language to a DB query. How much config do I need to do for this to work? What if the queries it makes are inefficient/wrong and my database gets hammered - can I customise them? How do I ensu…