I’ve been messing around with MCP servers to get a feel for the ecosystem. I’m seeing a real issue, and I’m not sure what solutions are possible with the architecture - am I missing something?

The LLM always sits in the middle of any pipeline. That means you’ll always have potentially messy and lossy translation in between every tool call (not to mention incredibly slow/wasteful compared to piping data between processes).

The example I was using: I wanted Claude to orchestrate some analysis on Stripe data for me. I asked it to get all transactions from last month and write them to disk (as a step one, before actually doing anything). Because the data coming out of Stripe goes back through the LLM before going to disk, it completely borked it and wrote only a small fraction of the data.

I'm trying to piece together the puzzle that lets a chatbot do useful things for me in my life. Is there a future-state where this issue isn’t an inherent problem? Some workarounds I've thought of:

- have a python interpreter and have the LLM write code. But then what’s the point of an MCP server when you’d just use the Stripe python library or APIs? - have some kind of inter-MCP-server communication protocol At this point we're writing an OS for the LLM to live inside.