Differences Between RAG and MCP
So, Meagan twisted my arm to write this blog post. Something about “educating the industry,” “showcasing our thought leadership,” and “don’t make me ask you again.” (She’s very persuasive.) I saw the recent Compass announcement about launching their MCP server and figured it was worth clarifying what that actually means, especially since Maxwell Realty has already been quietly using this tech with our SLY assistant for a few months now. Consider this my (reluctant) attempt to break down the differences, benefits, and why every brokerage should care. Let’s get nerdy!
Let’s expand on the differences between RAG and MCP and how they apply to real estate:
Retrieval-Augmented Generation (RAG) enhances large language models by augmenting their knowledge with external data. It works in three main steps:
Query Processing: The AI processes the user’s question to determine what information is needed (for example, "What are the rules for open houses in Edmonton?").
Retrieval: The AI queries an external knowledge base, such as your brokerage’s policy documents, historical transaction data, or regulatory guidelines, to retrieve relevant documents or information snippets.
Augmented Generation: The AI adds these retrieved documents to its context window and generates a response that blends its pre-trained knowledge with the dynamic information.
This is especially valuable for policies, rules, and procedures that don’t change daily.
Model Context Protocol (MCP), on the other hand, extends the capabilities of language models by providing a standardized interface to request additional information or perform actions mid-response. MCP typically works like this:
Recognition: The model identifies it needs an external tool or live data (for example, the latest listings or showing requests).
Protocol Execution: The model issues a structured request (like {action: "get_listings", area: "Beacon Hill"}) to an external API or service.
External Processing: This request is handled by an integrated system such as your MLS API, CRM, or marketing dashboard that fetches real-time data or performs the requested action.
Continued Generation: The model seamlessly incorporates the new data or result into the conversation, delivering a complete and interactive response.
One important thing to note is that RAG systems often require custom integrations for each new use case, which can make them feel a bit fragmented and ad hoc. In contrast, MCP offers a more unified, plug-and-play experience for connecting AI to various tools and data sources. That said, RAG still has a role to play, most likely as a component within an MCP setup, where it can handle the "reading" part of the job, like fetching documents from a vector database. That’s why both systems need to work together for a truly complete AI agent. RAG powers factual document retrieval, like compliance manuals, rules, and historical transaction data, while MCP brings in real-time data and triggers actions that keep transactions moving. When you combine both, you get an AI assistant that’s not just smart but also genuinely useful for modern real estate workflows.
Hats off to Compass for investing heavily to build this tech. For other brokerages, MCP can definitely level the playing field, especially when paired with RAG to handle document and policy retrieval. Together, they create a powerful AI experience that bridges knowledge retrieval and real-time action in a way that’s tailored to the real estate industry.
