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 RAG and then inject the retrieved data into my prompts, should this replace it? Can I integrate this in a useful way even?
The use case of on your machine with your specific data, seems very narrow to me right now, considering how many different context sources and use cases there are.
> 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.
From the link:
> To help developers start exploring, we’re sharing pre-built MCP servers for popular enterprise systems like Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer.
I’m glad they're pushing for standards here, literally everyone has been writing their own integrations and the level of fragmentation (as they also mention) and repetition going into building the infra around agents is super high.
We’re building an in terminal coding agent and our next step was to connect to external services like sentry and github where we would also be making a bespoke integration or using a closed source provider. We appreciate that they have mcp integrations already for those services. Thanks Anthropic!
I've been implementing a lot of this exact stuff over the past month, and couldn't agree more. And they even typed the python SDK -- with pydantic!! An exciting day to be an LLM dev, that's for sure. Will be immediately switching all my stuff to this (assuming it's easy to use without their starlette `server` component...)
It's not exactly immutable, but any backwards incompatible changes would require a version bump.
We don't have a roadmap in one particular place, but we'll be populating GitHub Issues, etc. with all the stuff we want to get to! We want to develop this in the open, with the community.
Are there any resources for building the LLM side of MCP so we can use the servers with our own integration? Is there a specific schema for exposing MCP information to tool or computer use?
If you have specific questions, please feel free to start a discussion on the respective https://github.com/modelcontextprotocol discussion, and we are happy to help you with integrating MCP.
Second, a question. Computer Use and JSON mode are great for creating a quasi-API for legacy software which offers no integration possibilities. Can MCP better help with legacy software interactions, and if so, in what ways?
Probably, yes! You could imagine building an MCP server (integration) for a particular piece of legacy software, and inside that server, you could employ Computer Use to actually use and automate it.
The benefit would be that to the application connecting to your MCP server, it just looks like any other integration, and you can encapsulate a lot of the complexity of Computer Use under the hood.
If you explore this, we'd love to see what you come up with!
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).
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 RAG and then inject the retrieved data into my prompts, should this replace it? Can I integrate this in a useful way even?
The use case of on your machine with your specific data, seems very narrow to me right now, considering how many different context sources and use cases there are.
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