I just explained why these issues don't apply to the LLM writing and invoking code: again, this is because code can apply successive transformations to the input without having to feed the intermediate results into the LLM's context. That code can read a file that would weigh in at 50,000 tokens, chain 100 functions together, producing a line that would be 20 tokens, and only the LLM will only see the final 20 token result. That really is only 20 tokens for the entire result -- the LLM never sees the 50,000 tokens from the file that was read via the program, nor does the LLM see the 10s of thousands of tokens worth of intermediate results between the successive transformations from each of the 100 functions.
With MCP, there's no way for the LLM to invoke one tool call that expresses "compose/pipeline these 100 tools, please, and just give me the final result" -- the LLM must make 100 individual tool calls, manually threading the results through each tool call, which represents 100 opportunities for the LLM to make a mistake.
It sounds like you are disagreeing with what I am saying, but it doesn't look you're giving any reason why you disagree, so I'm a bit confused.