MCP is prompt engineering all over again (simpleobservability.com)

MCPs (Model Control Protocols) are gaining traction as effective interaction layers for software leveraging large language models (LLMs). Simple Observability’s recent experiences in designing an MCP server reveal significant parallels to prompt engineering—emphasizing the iterative process of tweaking descriptions, restructuring responses, and adjusting contexts to improve model behavior. This challenge is compounded by the nascent state of MCP design, which lacks established benchmarks or best practices. The authors found that while traditional API design focuses on clean architectures and user-friendly interfaces, MCPs must cater to LLMs, necessitating an entirely different approach grounded in maximizing efficiency and minimizing cognitive load on the models. Key technical insights include the realization that MCP design involves compressing workflows over maximizing composability, as seen in examples where multiple API calls are combined into single, streamlined tools to reduce context overhead. Furthermore, the study highlights how LLMs struggle with conventional data formats, prompting designers to opt for more natural language queries instead of standardized APIs like Unix timestamps. As the AI/ML community continues to grapple with these evolving dynamics, it raises fundamental questions about the future of API design and the necessary conditions for eliciting desired behaviors from LLMs in real-world applications.