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Is the process for efficient use that first an MCP tool becomes in widespread mainstream usage, then that data trains a next model, then the next generation of models become highly proficient at using that particular MCP tool? Ive suspected that e.g. GPT works better in Codex and Claude works better in Vlaude Code because the harness tools have now been deeply trained into the model. Sure theCP json schema and its tools are sent in the prompt and such but I feel its more clunky and inefficient until enough training data optimizes efficient usage. Im not sure but it seems LLMs require incremental improvements based on practical usage.
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