MCP Demystified: How The Model Context Protocol Standardizes AI Tool Use

MCP, or Model Context Protocol, is an AI-related buzzword you might have encountered recently. The concept, however, is not overly complex and, despite any hype, offers significant utility.

To grasp what MCP is and the problems it solves, we first need to revisit the fundamentals of how Large Language Models (LLMs) and the AI applications that use them actually function.

Understanding the Foundation: How LLMs Really Work

It's crucial to understand that all LLMs are fundamentally token generators. They produce tokens, which are essentially words or parts of words. In short, they are sophisticated text generators. This was true in the past, is true today, and will remain true for the foreseeable future, even for advanced reasoning models.

You might argue that AI chatbots like ChatGPT can do much more than just generate text—they can perform web searches, run Python code, and conduct deep research. You would be correct. But the key is how they accomplish these tasks.

These powerful token-generating models operate within a larger application shell. ChatGPT, for instance, is an application that uses an LLM as a core component, but it's not just the model. The application contains a great deal of code, written by software engineers, that manages the LLM's operation.

This code injects a system prompt into the AI model behind the scenes. This prompt establishes guardrails, provides general instructions on behavior, and, most importantly, defines the available tools and when they should be used.

The Mechanics of AI Tool Use

The system prompt might contain instructions describing available tools with a specific format, for example:

WebSearch: <search_query>

When a user asks a question like, "Who is the current president of the United States of America?" the LLM processes this query. Because of the system prompt detailing the WebSearch tool, the model determines that the most probable and useful sequence of tokens to generate is one that calls this tool.

The LLM might output the following text:

WebSearch: current president of the United States

It's important to remember this is a probabilistic process. The model could still generate a hallucination, but the system prompt makes the tool-use output far more likely.

The application (e.g., ChatGPT) intercepts this specific output. Instead of displaying it to the user, it shows a message like "Searching the web..." while, in the background, code written by its developers executes a real web search using the query provided by the LLM.

The process looks something like this:

1. System Prompt (Invisible to User): Contains tool definitions.
2. User Question: The user's input.
3. Assistant's Tool Request (Invisible to User): The LLM generates the WebSearch token.
4. Tool Execution & Results (Invisible to User): The application runs the search and gets the results.
5. Final Generation: The search results are fed back into the LLM's context along with the original question. The LLM then generates a final, human-readable answer based on this new information, which is then shown to the user.

This entire workflow is orchestrated by the application, not the LLM alone. The LLM only requests to use tools that have been pre-defined and coded by developers.

Enter MCP: A Standard for AI Tool Integration

So, where does the Model Context Protocol (MCP) fit in? MCP addresses a core problem in the tool-use process: the lack of standardization.

Currently, every AI application has its own proprietary way of defining tools and exposing them to an LLM. If you developed a fantastic hotel ratings application, you would have to hope that the developers of various AI applications would manually integrate your service as a tool.

The MCP approach aims to standardize how tools are described, discovered, and used.

With MCP, you could build an "MCP server" for your hotel rating application. This server would use an official MCP SDK to describe its available tools—for instance, one tool to get ratings for a specific hotel and another to get ratings for a geographic region.

Because you're using the MCP protocol, these tools are described in a standardized way. An AI application that is an "MCP client" can then communicate with your server. The client's SDK can automatically extract the tool descriptions from your server and make them available to its LLM.

The developer of the AI application no longer needs to write custom code to integrate your specific tool. They just "install" or connect to your MCP server, and the SDKs handle the rest. You, as the MCP server developer, are still responsible for writing the actual code that executes when the tool is called. MCP standardizes the description and communication, not the underlying tool logic.

Beyond the Buzz: Is MCP Just a Rebranded API?

Some have argued that MCP is just a buzzword for what we've always called an API. To an extent, this is true. You could always connect an AI application to any API.

The key difference that makes MCP interesting is the ease of exposure and consumption. It creates a standardized, plug-and-play ecosystem for AI tools. Instead of manually wiring up every API on a case-by-case basis, an MCP server can be built once and then used by any MCP-compatible AI application.

The Future is Composable

It's likely we will see a growing registry of public MCP servers. AI applications that support the protocol, such as Cursor or Windsurf, already allow users to install MCP servers to add new capabilities.

As a user, you can install tools to customize your AI assistant for your personal workflow. As a developer building a specialized AI chatbot, you could pre-install a curated set of MCP servers to provide powerful, out-of-the-box functionality to your customers.

While the AI world is filled with a lot of hype, the MCP initiative appears to be a genuinely useful step forward. It provides a practical solution to a real problem, paving the way for a more standardized and interoperable ecosystem of AI-powered tools.