In my opinion, I get the desire to create some sort of specification for an LLM to interface with [everything else], but I don't really see the point at doing it on an inference level by smashing JSON into the context.

These models are usually very decent at parsing out stuff like that anyway; we don't need the MCP spec, everyone can just specify the available tools in natural language and then we can expect large param models to just "figure it out".

If MCP had been a specification for _training_ models to support tool use on an architectural level, not just training it to ask to use a tool with a special token as they do now.

It's an interesting topic because it's the exact same as the boundary between humans (sloppy, organic, analog messes) and traditional programs (rigid types, structures, formats).

To be fair if we can build tool use in architecturally and solve the boundary between these two areas then it also works for things like objective facts. LLMs are just statistical machines and data in the context doesn't really mean all that much, we just hope it is statistically relevant given some input and it is often enough that it works, but not guaranteed.