RAG vs MCP: What Is the Difference?
RAG is a way to answer from your own data. MCP is a way to connect an AI assistant to tools and data. They solve different problems, and they often work together.
Quick answer
RAG (Retrieval-Augmented Generation) is a pattern: look up the passages relevant to a question, add them to the prompt, then let the model answer. MCP (Model Context Protocol) is an open protocol: a standard way for an AI app like Claude or ChatGPT to call tools on a server, to search, read, write or act. They are not rivals. An MCP server's search tool is one way to do the retrieval in RAG.
RAG and MCP side by side
| RAG | MCP | |
|---|---|---|
| What it is | A pattern for grounding answers | A protocol for connecting AI apps to tools |
| Who decides to look things up | Your pipeline, usually before every answer | In chat assistants, usually the model, by choosing to call a tool |
| What the AI can do | Read retrieved passages | Search, read, write and take actions, as the server allows |
| Freshness | As fresh as your index | Whatever the server returns at the moment of the call |
| Setup | Build an index, choose chunking and search, keep it in sync | Add a server to an AI app that supports MCP |
| Across assistants | Inside the app you build | Any MCP client, such as Claude, ChatGPT or Cursor |
| Typical use | A chatbot over your documents or help center | An assistant that uses your notes and tools in a conversation |
For a longer explanation of the pattern itself, see What is RAG? and What is MCP?.
When RAG is the right choice
- You are building the AI app yourself and control the model and the prompt.
- Every answer must be grounded, so retrieval should run on every question.
- You have a large, mostly fixed set of documents, like a help center or a policy library.
- You need control over ranking, chunking and citations.
When MCP is the right choice
- You want an assistant you already use, like Claude or ChatGPT, to reach your data.
- The AI should also write or act: save a note, update a ticket, run a query.
- Your data changes all the time, and a copied index would go stale.
- The same source should work in several assistants without building each integration.
- You do not want to run a retrieval pipeline yourself.
Using RAG and MCP together
RAG and MCP can work together. An MCP server can run retrieval behind its search tool, with full-text search, vector search or both. The assistant calls that tool, gets back the relevant passages, and answers from them. That is retrieval-augmented generation, delivered over MCP.
One trade-off to know: in chat assistants like Claude and ChatGPT, the model usually decides when to call a tool. It can skip it and answer from memory. An app you build yourself can call the tool on every question instead. If an answer should come from your data, ask the assistant to check it, or add an instruction that tells it to search first. Instructions help, but they do not guarantee a tool call.
Where Hjarni fits
Hjarni is a knowledge base with a built-in MCP server. Claude, ChatGPT and other MCP clients can search and read your notes, and create or update them, in the conversation you are already having.
Under the hood, Hjarni searches your notes with full-text search. There is no vector database, embedding model or chunking to manage, and Hjarni runs no AI model of its own. The assistant you connect does the thinking. You write the notes.
Common questions
FAQ
Is MCP replacing RAG?
No. MCP standardizes how an AI assistant reaches tools and data. Retrieval still happens, often inside an MCP server's search tool. What MCP removes is the need to build a separate integration for each assistant.
Which is faster to set up, RAG or MCP?
MCP, when the data source already has an MCP server: you add its URL in your assistant and sign in. A RAG pipeline means choosing a search store, indexing your documents and keeping that index in sync.
Do I need a vector database for RAG or MCP?
No. RAG can use full-text search, vector search or both. MCP does not prescribe how a server searches. Hjarni, for example, uses full-text search.
Which should I use for my notes?
If you want Claude or ChatGPT to use your notes in a conversation, use MCP: connect a notes app that has an MCP server. If you are building your own chatbot over a fixed set of documents, a RAG pipeline gives you more control over retrieval.
Does the AI always check my data over MCP?
Not in chat assistants like Claude and ChatGPT: the model usually decides when to call a tool, and it can answer without one. Ask it to check your notes, or add an instruction to search first. Instructions help but do not guarantee a tool call. An app you build can call the tool on every question.
Give your AI a memory. Free.
Connect Claude or ChatGPT to notes they can actually read and write.
Give your AI a memory. Free.