LLM FUNCTION CALLS DON'T SCALE; CODE ORCHESTRATION IS SIMPLER, MORE EFFECTIVE.

Summary from the TLDR AI newsletter

Giving large language models the full output of tool calls is costly and slow. Output schemas enable developers to get structured data, which can then be processed. Using code execution as a way to process data from MCP tools opens the door to scaling the work of AI models. However, allowing an execution environment to also access MCPs, tools, and user data requires careful design as to where API keys are stored and how tools are exposed.