MCP vs. RAG: How AI Agents & LLMs Connect to Data

Imagine you're short on time and need to use an AI agent to help you answer some questions quickly and accurately. You grab your mobile device, type in the first question, and nuhhh! The agent replies, "Sorry! I don't know enough to answer your question." Aren't AI agents supposed to know everything on the internet? You've probably heard someone say large language models are powerful, but on their own, they're kind of like brilliant interns with

literally no memory and no access to your systems. They can talk, but they don't know your data, and they certainly cannot act on your behalf. You know how everyone's always saying AI is only as good as the data you give it? They're actually totally right. Today, we're going to unpack two different ways to give agents access to data. I hope you're excited for more acronyms because we're talking about RAG and MCP. Now both aim to make models

smarter and more useful but in very different ways. RAG helps models no more by pulling in the right information, while MCP helps models do more by connecting them to tools and systems that drive work. Retrieval augmented generation and model context protocol, or RAG and MCP, are two methods that allow AI to be able to provide more insight, answer questions, help users while being grounded in actual information. That information could be all kinds of things:

documents, PDFs, videos, websites, even systems or applications. While these two seem similar at first glance, they have some significant differences that set them apart. Let's use an example to explore this. Imagine: you're using AI to get assistance because you are going on vacation as an employee. Yes, I've been needing a vacation. You'll probably need to get some information about the vacation policy.

Perhaps check how much information that you have, review the vacation accrual policy and even request time off so that it's logged correctly. Based on this example, let's dig into how MCP and RAG are similar and different. We're going to double click on three different categories: purpose, of course, then data, and lastly, process. Let's talk similarities first. I'll bill-build these into let's say a Venn diagram. I'll put the

similarities in the middle. RAG and MCP are very similar in many ways, some of which we just talked about. For example, they aim to provide information, of course. And the data they're accessing doesn't actually live in the large language model, but is instead provided by outside knowledge.

Both can also reduce hallucinations by grounding the model in real-time or specialized information. But, these same areas are where they truly start to differ. We're going to start with RAG and then, we'll talk about MCP. Now RAG's main purpose is to add information, okay? I'm talking about providing large language models with additional information living inside context. It allows large language models to access and reference

proprietary or specialized knowledge bases, so that the generated responses are grounded in up-to-date and authoritative information. RAG is all about getting data that's static, semi-structured, or even unstructured, like documents, manuals, PDFs, and more. RAG also provides the user with the source of information from an answer, helping ensure that the answer can be checked and verified. RAG works in five different steps. I'll outline them

over here. We'll start with ask, of course. This is when the user submits their question or prompt to the system. Leaning on our vacation example, this would be, for example, "What is our vacation policy?" Next, we'll go into retrieval. This is when the system transforms that prompt into a search query and retrieves the most relevant data from a knowledge base, perhaps from an employee handbook. Let's assume it's in PDF format. The next piece is all about return.

over here. We'll start with ask, of course. This is when the user submits their question or prompt to the system. Leaning on our vacation example, this would be, for example, "What is our vacation policy?" Next, we'll go into retrieval. This is when the system transforms that prompt into a search query and retrieves the most relevant data from a knowledge base, perhaps from an employee handbook. Let's assume it's in PDF format. The next piece is all about return.

This is one that return passage that was received, right, or sent back to the integration layer for use in context building. Then we'll move to augmentation. This step is all about when the system is building an enhanced prompt for the large language model, combining the user's question with all that retrieved content. Andlastly, of course, the part that we know the most and well: generation. This is when the large language model is going to use that augmented

prompt to produce a grounded answer and returns it to the user. For example, let's say there's a passage in that handbook that says employees accrue one day of vacation time every pay period. Building on our example of vacation time for an employee, RAG would help us read through the employee handbook, any payroll documentation to understand maybe the company's vacation policy, how it works, how employees accrued time off, and more. MCP, on the other hand, is different. MCP's

main purpose is to take action. It's a communication protocol that allows the agent to connect to an external system, either to gather information, update systems with new information, execute actions. It's even orchestrating workflows or going to get live data. So I'll put systems here. MCP works in a different set of five steps. We'll start with discover. This is when the large language model is connecting to an MCP server, and

takes a look at what tools, APIs, and more are available. For example, if you asked for our vacation story, "How many vacation days do I have?" it would take a look and see if it had access to maybe the payroll system or wherever that information lives. The next step is all about understanding. This is when it's reading each tool's schema. I'm talking about the inputs and outputs to know how to call it, how to reach out. Then we'll go into plan. This is when the

large language model is deciding which tools to use and in what order to answer the user's request. Moving along, we'll go to execute. In this phase, it's all about sending structured calls through the secure MCP runtime, which runs the tools and returns the results. And lastly, integrate. This is when the large language model is using those results I was just talking about to keep reasoning, make more calls if needed, or of course, finalize an answer or an

action. When it comes to the process of vacation time for an employee, the AI would use MCP to pull the employee's open number of vacation days from an HR system and perhaps even submit a request to their manager for additional days off through that same system. We've unpacked the similarities and differences between RAG and MCP today, and it all comes down to their end goal, data and how they work. RAG is all about knowing more. While on the

action. When it comes to the process of vacation time for an employee, the AI would use MCP to pull the employee's open number of vacation days from an HR system and perhaps even submit a request to their manager for additional days off through that same system. We've unpacked the similarities and differences between RAG and MCP today, and it all comes down to their end goal, data and how they work. RAG is all about knowing more. While on the

other hand, MCP is about doing more. If you're thinking ahead, you may be wondering 'Could these ever work together?' AI use cases need all kinds of data after all. You're on the right track. There are times that MCP uses RAG as a tool to be even more effective at information return for a user. If you're planning your next AI project, the key isn't choosing one pattern or the other. It's understanding when to retrieve knowledge, when to

call tools and how to architect both for things like security, governance and scale.