Building Retrieval-Augmented Generation (RAG) applications has become a cornerstone of modern AI, enabling large language models (LLMs) to ground their outputs in up-to-date, domain-specific knowledge. Traditionally, RAG pipelines relied on embeddings and vector databases to fetch relevant context at inference time. However, as AI systems grow more complex and the demand for real-time, secure and scalable integrations rises, Model Context Protocol (MCP) is emerging as a transformative standard — not just for tool invocation, but for the…