RAG vs MCP: Full Guide For 2025

LLM models are improving rapidly with lots of data. But they still have some drawbacks that act as a constraint.

The most effective AI systems can retrieve data in the right format and reason intelligently through it. But they are only fixated on the data they have been trained on the tools they are connected with APIs. 

With this, two cutting-edge technologies have emerged that are reshaping how LLMs perform. Those are

1. Retrieval Augmented Generation (RAG)
2. Model Context Protocol (MCP)

And, RAG vs MCP is a growing topic now.

Lets understand how they are working together because both methods are created to overcome the limits of traditional LLMs.

So, let’s find out how.

What Is RAG?

RAG combines the power of language generation with real-time information retrieval.

Unlike others, RAG fetches data from external sources and feeds it into the model to generate more accurate and better results. So that you don’t have to worry about getting any relevant information.

Traditionally, GPT & BERT-based models are limited to the information that they are trained on.

But RAG overcomes this significantly.

With RAG pipelines, you can improve the overall relevancy and accuracy of LLM responses. With no more retraining, you will get live updated data anytime and anywhere.

Tools & Frameworks Used In RAG

Here’s the list of tools and frameworks used in RAG.

1. Vector Databases (Semantic retrieval)

Vector databases are a special type of database that is designed to store data in the form of vectors. To learn more about vectors, you can check this guide.

Vector databases can give you semantically searchable data.

Here are some examples

• FAISS – Facebook’s open-source vector search engine

• PineCone – Fully Managed, Scalable Vector DB

• Chroma – Really lightweight and completely developer-friendly

2. Framework And Orchestration

There are different types of frameworks, and let’s understand each of them.

1. Langchain – It is a popular framework built for building LLM pipelines (retrieval chains)
2. LlamaIndex – It is great for document ingestion and retrieval interfaces.
3. Haystack – NLP-focused retrieval framework with UI support

3. LLMs (Generators)

Here is the list of LLMs that will be used. 

• OpenAI GPT-4/GPT- 4o
• Anthropic Claude 3
• Google Gemini

Let’s Understand The RAG Pipeline

We will understand it in a 5-step process

1. Data Intake:

Imagine a new pile of information (any documents) came in, and you need to analyse it. These are the new raw materials that need to be processed and used.

2. Chunking The Data:

RAG takes the pile of data and breaks it into bite-sized pieces, and it segments them clearly so that whenever someone raises a query, depending on the query, it will send the right information. Making it highly precise.

3. Embedding:

This is when the real AI magic starts happening. With all the chunks of data present inside the RAG, it starts finding the right contextual answer based on the query given by the user.

4. Vector Storage:

Now, all your new information is going to be stored in the vector storage, and it can be used whenever a user asks for a query. For that you can use Pinecone or Chroma.

5. Retrieval

Now, the game starts once the user drops a question or query. RAG helps you find the exact answer based on the semantically correct information. 

Then, you get your correct piece of information within just a few seconds.

Key Features & Benefits Of RAG

RAG brings you amazing benefits that can help you in multiple ways.

1. Dynamic Knowledge Access

RAG allows AI to access knowledge that was not part of its trained data, including real-time or domain-specific information.

2. Improved Factual Accuracy

As answers are primarily grounded in external sources, RAG reduces hallucination that often stops LLMs

3. Scalability

It easily integrates with enterprise databases, internal wikis, APIs, or document repositories to scale AI knowledge.

4. Reduced Retraining Costs

With RAG’s vector databases, it can search information in real time so that you can expand your knowledge base without retraining the old model itself. By simply updating the knowledge source, you will get the updated information in real time.

5. Fast Iterations

Got a new file? Put it in the RAG and re-embed it, then your RAG will instantly get updated with the new chunk of data. No need to wait weeks for retraining.

Limitations Of RAG

Even with multiple advantages, RAG has some limitations that we need to be aware of.

• RAG doesn’t auto-refresh content, and it needs timely updates of data.
• Retrieval often depends on vector similarity; it might fetch unrelated but semantically close documents.
• RAG doesn’t update the model or retriever based on user corrections unless manually updated or retrained.

What Is MCP?

Let’s understand MCP in a deeper way. As it is taking the market by storm because of the efficiency it brings to the table.

You know, the problem with LLMs is they can’t fetch data from any specific tool directly; you need to give it to them. That task becomes monotonous.

Now, your LLMs can talk to all of your databases.

That’s where MCP comes in, as it is a system-level feature that allows LLMs to remember context and data from a specific tool.

With that, it provides persistent memory, personalization, and stateful interactions. It wants to become the universal connector between AIs and other tools.

Here are the four things that go in MCP.

1. Users
2. AI (LLM)
3. MCP Client
4. MCL server
5. Tools

Now, let’s understand how they work together.

Step 1: User asked for information: “What’s the sales data overview of last month?”  LLM received the query.

Step 2: LLM sent the data to the MCP client, which is an app-side agent that is integrated with LLM. MCP client communicates with MCP server.

Step 3: MCP server, which is also known as a central memory manager, will securely store your contextual information about users and make it accessible to different clients and sessions.

Step 4: Now, it gathers information from different tools and sends it to the user as per the given query.

It is solving the context problem of LLMs at the fastest pace possible.

Tools And Frameworks Used In MCP

1. Structured Memory Storage

This is the strength of MCP that remembers context. 

• PostgreSQL/MongoDB – With these both, it becomes easy to manage user-specific data over time.

• Firebase/Supabase – With these, you will get real-time updates, and it’s quite simple to set up.

2. Context Management Tools

There are different types of context management tools in MCP and let’s understand them

• LangChain Memory Modules – LangChain provides built-in “memory” abstractions that allow you to manage context effectively in conversational or retrieval-based pipelines.
• LlamaIndex (Context Engine) – It is a framework that is built around ingesting structured/unstructured data and dynamically retrieving and formatting it for LLMs.

3. LLM Integration

• OpenAI & ChatGPT API
• Anthropic claude memory APIs
• LangChain agents + Tools

Key Features & Benefits Of MCP

1. Persistent Memory

• This helps in storing long-term contextual information (i.e., user preferences, project status)
• Survives across multiple sessions, unlike traditional stateless LLM chats.

2. Structure Context Representation

• Context is saved in well-defined schemas (like JSON)
• Each piece of memory can have:
  Type, timestamp, source, confidence or priority score.

3. Memory Injection Into LLM Prompts

• Clients pull relevant memory and dynamically insert it into the prompt context for the language model.
• Enables personalization without fine-tuning the base model.
• It might become difficult to manage it at scale due to context overload issues.

Limitations Of MCP

MCP provides connection to different tools quickly but that also comes with multiple drawbacks. Let’s see each of those

• With multiple connections in MCP might confuse the LLM to fetch the right information from the right tool given the context overload issue.
• Malicious memory inputs or insecure context providers can manipulate LLM behaviour by putting in fake tools and data.

Key Differences Between MCP And RAG

Feature	RAG (Retrieval Augmented Generation)	MCP (Model Context Protocol)
Core Function	Fetches external knowledge to answer user queries	Stores and manages user-specific memory/context
Primary Goal	Improve factual accuracy and domain relevance	Enable long-term memory and personalisation.
Data Source	External document store or knowledge base	Internal structured memory (context objects)
User Specific?	No, RAG retrieves general info (not tied to a specific user)	Yes, MCP is user-centric, focused on individual memory
Requires Prompt Injection?	Yes, retrieved docs are fed into the LLM prompt	Yes, context memory is also injected into the LLM prompt
Cross-Session Memory	No, stateless unless paired with other systems	Yes, designed for long-term memory across sessions
Cross-App Integration	No, typically app-specific	Yes, supports context sharing across multiple apps/tools
Retraining Dependency	Low, avoids retraining by using external sources	None, only updates memory objects, not the model itself

Let’s Understand The Use Cases Of Both

This is important to understand when you can use RAG or MCP

When To Use RAG?

• You can use it when you need real time answers or from a specific domain.
• The LLM can directly fetch data from the vector database of the RAG.
• It will be really helpful for multiple industries like Healthcare, Legal, B2B as well as eCommerce.
• You can access it by using tools like Pinecone, LlamaIndex

When To Use MCP?

• If you’re dealing with multiple tools at a time with MCP you can link to all of them and fetch data as per need.
• Useful in all industries like Healthcare, Legal, Marketing, Sales, and many more.

When To Use Both Together?

Uses Cases	RAGs Role	MCPs Role
Healthcare Virtual Assistant	Retrieve real-time medical guidelines	Track patient history and past symptoms
Enterprise Sales Copilot	Pull latest product sheets and lead info	Easily recall deal stages, past objections
HR Assistant	Fetch HR policies of a specific company	Remember user’s job level, team, past queries

Conclusion

Perhaps you might be thinking RAG and MCP are competing with each other. But they are not.

They are complementing each other at different levels, making LLMs perform quite specifically. RAG and MCP will help you to extend the capabilities of AI.

RAG will help you retrieve real-time data, and MCP will help you connect different data sources and tools easily. Depending on the use cases, you can use any of them or both of them.

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