MCP vs RAG: how they overlap and differ

The Model Context Protocol (MCP) and retrieval-augmented generation (RAG) are two approaches that enable large language models (LLMs) to access and utilize external context, with RAG allowing LLMs to generate responses based on relevant external information and MCP facilitating interactions between LLMs and outside data sources via an MCP server. While both methods allow LLMs to access data and functionality from external sources, they are suited for different use cases, with RAG being ideal for enterprise AI search and MCP supporting agentic AI use cases where users want to perform actions within applications. Merge, a product integration platform, supports the use of both MCP and RAG by providing access to normalized customer data, access control lists, and a dedicated MCP server, enabling businesses to leverage these technologies to power their product's AI features. By understanding the strengths and weaknesses of each approach, companies can effectively integrate LLMs into their products and automate processes, improving user experiences and streamlining operations.

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