What MCP is, in one paragraph
The Model Context Protocol is an open standard, first published by Anthropic in November 2024, for connecting AI applications to outside tools and data. An MCP server describes what it can do (tools the model can call, resources it can read, prompt templates) in a machine-readable way, and any MCP client, such as Claude, Cursor, VS Code or ChatGPT, can connect to it without custom glue code. Messages are JSON-RPC 2.0, sent over standard input and output for a local server or over HTTP for a remote one.
What RAG does
Retrieval-augmented generation splits your documents into chunks, turns each chunk into an embedding, and stores them in a vector database. When a question arrives, the pipeline embeds the question, finds the closest chunks, and adds them to the prompt so the model answers from your material instead of from memory.
RAG is good at one thing: grounding answers in a large, mostly static body of text such as docs, policies, tickets or contracts. It does not take actions, and its answers are only as current as the index.
Where they differ
In classic RAG the retrieval happens before the model runs, on every question, whether the model needs it or not. With MCP the model decides during the conversation that it needs something and calls a tool to get it. That makes MCP better for questions that need live data (today's orders, the current state of a ticket) or several steps (look up a customer, then their invoices, then draft a reply).
MCP also covers writing. A RAG pipeline can read your wiki; an MCP server can read it and also create the page.
Using them together
The common pattern is "agentic RAG": expose your retrieval as an MCP tool, for example search_docs(query), and let the model call it when it decides it needs context, as often as it needs, with its own query wording. Many vector databases now ship MCP servers for exactly this, including Qdrant, Chroma, Pinecone and Weaviate.
That keeps the parts of RAG that work (good chunking, embeddings, reranking) and replaces the fixed "always retrieve first" step with the model's own judgement.
What to check
A retrieval server has read access to whatever you indexed. Check that the server only reads, that a remote endpoint requires auth, and where your queries and documents go if the server is hosted by someone else. Retrieved documents can also carry instructions aimed at the model, so treat a server that returns raw third-party web content with more care than one that searches your own files.