The rapid evolution of artificial intelligence has driven organizations across sectors to develop agents that genuinely augment human expertise and automate complex tasks. Retrieval‑Augmented Generation (RAG) has proven to be a promising paradigm: it unites large language models with external knowledge retrieval to boost factual accuracy and domain relevance. However, the seamless integration of heterogeneous tools and context sources remains a thorny challenge. The Model Context Protocol (MCP) is a lightweight and extensible framework designed to unify the exchange of structured context between RAG‑powered agents and external services. MCP defines a clear JSON schema for context requests and responses—encompassing metadata, user session state, and tool interfaces.