Over the course of the last few months and years, LLMs have taken an important place in our lives, be it writing, learning, researching, building software, or asking questions. Nowadays, users have more and more LLM options than ever.
However, LLMs still have relevant limitations:
- Most LLMs can't access real-time data.
- They fail to remember conversations beyond context windows.
- They prove a significant need for accurate prompt engineering.
Retrieval Augmented Generation (RAG) helps to resolve some of these gaps by giving the LLMs a tap to access external information, but even RAG alone is not enough. And here’s where the Model Context Protocol (MCP) comes in: combined with RAG, it can lead to incredibly optimized results.
MCP
MCP allows interaction and communication with external systems in a unified manner. It works as if it were a USB-C port, yet the port is for AI. A single one works for all connections and allows the LLM to talk to any system, be it APIs, DBs, or other external tools.
Rather than building a separate communication channel for each tool, they can all be put into an MCP server, so that the developer can then expose them. Any MCP client that is compatible with MCP understands what tools are available in the MCP server, and then the MCP server declares the most relevant ones.
Why is MCP Important?
Historically, every LLM has always required an additional code to have support for each individual tool. Now, MCP can solve this problem by providing a simple yet efficient solution based on a clean architecture.
RAG and MCP, not RAG vs MCP
In agentic AI, both RAG and MCP have separate tasks and action lists. RAG helps in understanding rules, and being knowledge-rich, while MCP helps with tools and real-time data. But if the two of them are combined together, they create the foundation of "Agentic AI".
Real World MCP Integration
MCP usage is increasing in many industries, such as:
- Coding Assistants in Dev environments
- Automated Marketing
- Analytics
- Research
Let's say we have a to-do list and we need to add something to our list. With MCP, it becomes really simple to create a platform that helps with updating the to-do list. How? The AI app will connect to the server; the MCP tool will present the right tools to update the to-do list; the AI app will make the addition. The good thing is that the resources exposed by the MCP server are only the ones relevant for this specific task, which ensures security and modularity.
Is MCP only for Tech Geeks?
MCP was designed in a way to make it easier for developers to streamline LLM connectivity and the tasks it can perform, but there are ways it can be used that do not require a great deal of technicality. Some interfaces, for example, make it easy for a non-code person to integrate and use MCP in their custom applications. However, it is still a platform that depends on developers to create and maintain MCP servers, and non-technical people mostly avail the services MCP provides.
Security
With MCP comes great power and a new way of communication, but it also brings in more responsibility. Only secure and verified MCP servers must be used. There must be guardrails put in place against prompt-injection, and exposure to tools should be safe. If deployed correctly, MCP is a true game-changer.
The Future
We are entering a new world of operational AI systems. AI has to not only be a thinker, but also a doer, and MCP provides the ideal platform for it. It is emerging as a layer that transforms what LLMs can do, and is definitely one to watch for the near future.
