Examines the structural differences between Model Context Protocol (MCP) and natural language Skills in the development of AI agents.

While MCP offers a standardized, deterministic framework for connecting models to external data through rigid code-based schemas, Skills provide a flexible, instruction-driven approach that uses natural language to guide agent behavior.

The sources contrast these methods across several dimensions, including technical complexity, execution latency, and security risks like arbitrary code execution versus prompt injection. MCP is highlighted as ideal for high-stakes, enterprise-scale tasks requiring centralized updates, whereas Skills excel in rapid iteration and capturing specific organizational "taste."

Ultimately, the text advocates for a hybrid architecture that combines the reliability of MCP "hands" with the cognitive nuance of Skill-based "brains." This integrated strategy aims to overcome common pitfalls such as context bloat and performance degradation in production environments.