MCP = HTTP or USB-C for the AI World?
Model Context Protocol (MCP) has been gaining a lot of attention lately. It’s an open standard designed to help AI applications—especially large language models (LLMs)—connect more easily with external data sources and tools.
This article offers a clear look at some key things you should know about MCP.
Why Was MCP Created?
Traditionally, connecting a new data source used to mean building a custom connector from scratch. Each model was isolated in its own data silo, making it difficult to quickly access the right context. It was like the early days of electronics, where each phone had its own charger. Every AI application had a unique way of integrating tools, resulting in high development and maintenance costs.
MCP was introduced to tackle exactly this problem. Open-sourced by Anthropic in November 2024, it aims to simplify how LLMs connect to external data and tools.
Think of it as a universal adapter for AI—offering a standard, streamlined way for models to plug into the broader ecosystem.
Architecture
A typical architecture using MCP looks like this:
Host: Usually the main application running the LLM—like Claude Desktop, a smart IDE, or a chatbot.
Client: Acts as a bridge between the host and the server, handling message routing and protocol negotiation.
Server: A lightweight, standalone program. Each server wraps a specific type of data source or functionality and communicates with the client using a standard protocol. It’s responsible for providing context data or carrying out actions when called.
If you’re curious to dive deeper into the details of the MCP protocol, check out this website.
Agent, RAG, and MCP
To make the relationship between Agent, RAG, and MCP easier to understand, think of them as parts of a modern library system.
Agent (The Smart Librarian): Imagine an intelligent librarian who helps answer visitors’ questions. When someone walks in with a query, the librarian figures out what they’re really asking—and decides the best way to find the right answer.
RAG (Retrieval-Augmented Generation): This is the library’s advanced search engine. When the librarian needs help answering questions, they turn to this system to dig through books, databases, or even online sources. Then, using that information, they craft a well-informed response.
MCP (Model Context Protocol): Think of this as the library’s communication framework. It’s the behind-the-scenes protocol that allows the librarian to smoothly interact with all the systems inside the library—like the catalog or the database—as well as external sources, like other libraries or digital archives.
Real-world applications of MCP
MCP has some really practical use cases in AI coding tools like Cursor.
Imagine you’ve built a web application and written a bunch of code.
Normally, the workflow goes like this: you preview the code in your editor, open it in a browser, run into an error, copy the error message, switch back to Cursor, paste it in, get an explanation, fix the code, and then refresh the browser to see if it worked. It’s a lot of back and forth.
But with Cursor powered by the MCP protocol, that whole process changes.
Now, Cursor can handle most of that for you—automatically previewing your code, checking how your application runs, capturing error messages, fixing bugs, and debugging over and over until everything just works.
Insights
Here are a few personal thoughts.
Ecosystem Development
MCP is originally being led by Anthropic as an open-source initiative, and it’s already gaining some traction with partners and the open-source community—including companies and developer tool vendors mentioned earlier. OpenAI, among others, has started making its own SDKs compatible with MCP tools.
That said, the value of any protocol really comes down to ecosystem support.
One challenge here is the battle of standards: big players like OpenAI and Microsoft may prefer to push their own plugin or tool integration frameworks, and we might see multiple competing approaches emerge from the open-source world as well.
Security and Permission Control
Giving models access to real-world data and tools inevitably raises security concerns. On one hand, a model might unintentionally request sensitive information; on the other, a malicious actor could try to trick it into triggering harmful actions.
MCP takes these risks seriously—it includes built-in protections like Roots to define resource boundaries and requires user approval before tools are called. It offers a unified interface, but strong security still needs to be layered on top, especially in enterprise environments.
One key challenge is integrating MCP with existing authentication and auditing systems to ensure it fits within a company’s broader security framework.
Performance and User Experience
Bringing MCP into the loop inevitably adds an extra layer of communication between the model and the final result. While JSON-RPC messages are generally lightweight, frequent or high-volume tool calls can add up, potentially introducing noticeable delays.
To address this, MCP supports streaming responses (via HTTP SSE) and concurrent request handling. It allows servers to push results as they’re ready, and lets clients wait on multiple requests in parallel.
Still, in ultra time-sensitive scenarios—like voice conversations that require multiple tool calls per second for real-time sensor data—performance could remain a bottleneck. To fix that, there's still for ongoing work at the lower levels, such as more efficient encoding, leaner network transport, or support for batched requests.
Another important piece of the user experience is trust and transparency. Users should be able to see what external data or tools an AI assistant is using. The MCP client should clearly show which documents were referenced, which tools were called, and provide traceability so users feel confident in how results were generated.
The Future of MCP
In summary, MCP is still in its early days, but its open and standardized approach aligns well with where the AI world is headed.
If history is any guide, open standards often lay the groundwork for widespread adoption—just like USB brought order to device connections, and HTML became the foundation of the web.
So here's a thought: could MCP become the go-to interface standard for the era of AI—unifying how they access data and tools? What do you think?