What is the difference between RAG and MCP?

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RAG and MCP solve different problems, and in practice they're often used together rather than as competitors. RAG (retrieval-augmented generation) is a technique for making an LLM's answers more accurate by pulling in specific documents or data at the moment you ask a question, instead of relying only on what the model memorized during training. MCP (Model Context Protocol) is a standardized way to connect an LLM to outside tools and systems — databases, file storage, search APIs, internal company software — so it can fetch that information or take action, instead of every app building its own custom, one-off connection.

How do RAG and MCP differ? Here's a simple way to think about it: RAG is the strategy (look things up before answering), and MCP is plumbing that can carry out that lookup consistently across different AI apps and vendors, instead of every team reinventing the wiring. What it is — RAG: a pattern that retrieves relevant text, then generates an answer using it; MCP: a protocol, a standard way to plug an LLM into tools and data sources. Problem it solves — RAG: answers grounded in real documents instead of memorized guesses; MCP: consistent, reusable connections instead of one-off integrations per app. Example — RAG: a chatbot answers from your company's PDF handbook; MCP: a chatbot connects to your company's database, calendar, or ticketing system.

Can you use RAG and MCP together? A support chatbot might use MCP to connect to your company's knowledge base, then apply a RAG pattern on top of that connection to answer questions with real citations. You don't have to choose one over the other — most serious AI assistants today lean on both.

Who maintains MCP? MCP started as an Anthropic project in late 2024. In December 2025, Anthropic transferred MCP's governance to the Agentic AI Foundation (AAIF), a new Linux Foundation project co-founded with Block and OpenAI, with AWS, Bloomberg, Cloudflare, Google, and Microsoft also on board as founding members — shifting MCP from one company's tool to a shared, actively evolving industry standard.

	RAG	MCP
What it is	A pattern: retrieve relevant text, then generate an answer using it	A protocol: a standard way to plug an LLM into tools and data sources
Problem it solves	Answers grounded in real documents instead of memorized guesses	Consistent, reusable connections instead of one-off integrations per app
Example	Chatbot answers from your company's PDF handbook	Chatbot connects to your company's database, calendar, or ticketing system