What is the Difference Between MCP and RAG?

Large language models are a treasure trove of data, but until last year, their use was limited to basic Q/A based on their training data. Then came the concept of RAG, a breakthrough that helped us connect our data sources with LLMs to create personalized, credible systems. Now with MCP, we are taking the way we work with LLMs a step ahead by connecting them with external tools. So, is RAG vs MCP a thing, or are these complementary technologies that can enhance the outputs we get from LLMs? In this article, we will break down the differences between the MCP and RAG and understand how we can use the two together to build sophisticated solutions with LLMs.

What is RAG?

RAG or Retrieval Augmented Generation combines the power of information retrieval into the generation process. Usually, LLMs solely rely on their training data to generate the responses for user queries, which would sometimes lead to incorrect or biased results. With RAG, LLMs can retrieve external information during the output generation process, bridging the gap between LLM’s static training knowledge and dynamic information.

Here is how a RAG system works:

• Query: The user’s input to the LLM acts as the query for the RAG system.
• Retrieval: Before LLM generates a response, the “retrieval” process within the RAG system goes through a knowledge base relevant to the query to find the most relevant information.
• Augmentation: The most relevant retrieved information is then “augmented” to the original query, and then this cumulative information goes into the LLM.
• Generation: The LLM uses the combined input (query + retrieved information) to generate a much more accurate and relevant response. Finally, this response is shared with the user. 

RAG-based systems are typically used for tasks that require the outputs to be accurate, thorough, and well-researched. That’s why such systems are widely used in tasks like:

1. Customer Support: To ensure that the responses to the customers are based on up-to-date information. 
2. Enterprise Search: To help companies build reliable search engines to help their employees find relevant company information.
3. Personalized Recommendations: To help recommendation systems serve users better by suggesting products and services based on their choices and previous behavior. 

Not just these, RAG systems are being widely used for tasks like legal assistance, healthcare research, financial reporting, and more. However, despite their advantages, RAG systems come with their own set of challenges, like context window limitation, retrieval inaccuracies, latency, and setup complexities. 

What is MCP?

MCP or Model Context Protocol was launched by Anthropic in 2024, but it is in 2025 that the world is finally recognizing its potential. MCP allows LLMs to seamlessly connect with external tools, APIs, and data sources in real-time. This open standard enables LLMs to go beyond just text generation and helps them to perform actions, trigger workflows, and access current information to support active decision making.  

The key components of MCP are:

• Model: The model or LLM is the engine that runs this framework and is responsible for the output you receive. The model can be accessed using a “Client” like a Claude desktop app, an IDE or a chatbot.
• Context: It is the extra information that a model needs to answer your query accurately. The context is held within a system called “Server”. This can be a Google Drive, GitHub repository, mail box, PDFs, etc. 
• Protocol: This is the set of guidelines that allows a model to access different sources like external tools and APIs to gain the relevant context in regards to that query.

When user inputs a query, the client sends a request to the server to get relevant information. The server provides  client with the required context, which then client uses to provide user with a response or complete a task. Thus, MCP allows the LLMs to think and use the tools at its disposal to perform actions and provide reliable responses.  

MCP can be greatly useful for building systems that require:

1. Real-time data access: Like a stock market analysis app, an inventory management system, or an order-taking application.
2. Task automation: Like updating CRM, sending emails, scheduling meetings, and more.
3. Triggering Workflows: Like an employee onboarding process or deploying a code.

Overall, MCP removes the need for manual data uploads or creating custom integrations for different tools. It also allows LLMs to work with local and cloud-based systems, expanding their usefulness from simple Q/A tools to actual action-taking systems. 

Checkout: How to Use MCP?

MCP vs RAG: Competitors?

No, MCP and RAG are not competitors in the way they work or the tasks they perform. As we have discussed in the previous sections, MCP and RAG perform different tasks and empower LLMs in different ways. RAG powers LLMs with additional data while MCP grants LLMs the ability to act. The key differences between MCP and RAG are summarised in the table below:

Can MCP and RAG work together?

Yes, MCP and RAG can work together to help us design highly sophisticated AI workflows. RAG allows LLMs to pull relevant information while MCP executes tasks based on retrieved knowledge. Using these two together, we can create the following workflows:

1. RAG as a tool within the MCP framework

In this case, an LLM operating with MCP can have RAG as one of its tools, which it can use to fetch the required information. 

Example: An MCP-powered AI system for a Marketing Campaign. It uses RAG to retrieve information regarding previous campaigns and competitor information. Then, using MCP-powered tools, it creates social media posts and schedules them across different platforms. 

2. MCP for guiding RAG-Powered Agents

In systems involving multi-agents, each agent can have its own RAG pipeline and MCP can act as a coordinator for the system.

Example: A MCP-powered multi-agentic customer support team: When a customer asks a query, based on the query MCP agent delegates this task to one of the tech support/order status/payment issues. That agent uses RAG to find the relevant information based on the query, and then it relays its output to the MCP agent. This agent finally conveys its response to the customer.

Together, the combination of MCP and RAG can be used to enhance LLM functionalities and help to build AI systems that can think and act.  

Which one should you pick?

The choice between RAG, MCP, or RAG + MCP depends on the task. Each of the frameworks has its unique strengths. Here is how you can decide which approach to take:

• RAG: If your main goal is to improve the accuracy, relevance, and factual grounding of LLM-generated content, then “RAG” should be your choice.
• MCP: If your main goal is to allow your LLM to interact with external systems, perform actions, or leverage tools to complete its tasks, then “MCP” is your go-to path. 
• RAG + MCP: If your goal is to build an intelligent, autonomous system that can better understand and act decisively, then the combination of RAG and MCP is your go-to option. 

Also Read: What is the Difference Between A2A and MCP?

Conclusion

Large language models have taken the world by storm! Yet, their use remains limited. With RAG, LLMs get access to external knowledge bases that can help LLMs generate much more informed responses. With the MCP, LLMs get access to tools that they can leverage to perform actions. RAG and MCP do not compete with each other, both frameworks serve different purposes. But together, RAG and MCP can work to help us build systems that are smart and efficient.