MCP vs RAG: What's the Difference?

MCP (Model Context Protocol) and RAG (Retrieval-Augmented Generation) are two distinct approaches used in B2B SaaS environments to manage data within AI-driven systems, each serving unique purposes within an agent stack. RAG is designed for grounding language model responses in external content, making it suitable for tasks such as answering questions across documentation and performing semantic searches over stable corpora, while MCP facilitates reading and writing current-state operational data by enabling structured communication between LLM clients and external servers. The choice between using RAG and MCP depends on how frequently the data changes and whether the agent needs to reason about or act on it; for instance, RAG is ideal for stable knowledge retrieval, whereas MCP is more effective for real-time data access and updates. A hybrid approach, where RAG is used for finding relevant objects and MCP for fetching current states via API calls, is often the most practical, with each method offering distinct benefits in terms of authorization and data accuracy. Unified provides an API layer and MCP server that cover both RAG and MCP, allowing for authorized access to a variety of data sources without storing end-customer data, thus enabling efficient and reliable agent operations across diverse SaaS platforms.