Hey guys, it's Sean here. Let's talk about MCP. Since its release in 2024 November, MCP has been getting hotter and hotter.
So, in today's video, I'm going to try to explain it in 15 minutes. Let's forget about all those technical jargon, all those complicated concepts. Let's start with a simple story.
So, this is Lee. He's incredibly indoorsy, like unbelievably indoorsy. After graduating college and learning everything he thought he needed to know, he just locked himself in a basement room and never came out.
But Lee is a smart guy. Ask him about a book and he can read the whole thing in 30 seconds and give you a perfect summary. What is a stock market circuit breaker?
Boom. Like 2 seconds later, you get your answer. But if you ask him about today's weather or what Trump meant by his recent comment on reciprocal tariff, well, Leo never goes out and his room doesn't even have a window.
So he either has to admit he doesn't know or just make something up. So if you really need Lee to answer these outside world questions, he has to write a letter like a letter to the weather bureau or the local library. But you know how it is dealing with those bureaucratic institutions.
Communication with them has to follow their specific protocols or they will just send your request back. Lee had to create a templates that compile with their requirements before the first communication. This probably sounds manageable, right?
We just need to create one templates to establish a connection with each institution once then follow that templates going forward. But that's just a simple scenario. Now imagine there's not just sleep but multiple residents each handling their own tasks and each needing to communicate with up to 10 different institutions.
If all these residents wants to communicate with all the organizations, everyone combined would needs to create templates 50 times and that's freaking annoying. And this is a situation developers has to face before MCP appeared. Each residents represent a large language model or AI application.
They might have strong capabilities themselves but have no way to independently call external tools. Writing letters to institutions like calling external tools. Each external tools has its own connection standards to enable LM to call external tools.
They must create a function to help them pass messages. Because each resonance situation is different. This template is also kind of difficult to reuse between them.
This is function calling. It works but can be better. Let's go back to our example.
Now a dude called MTP noticed this problem and says hey first of all everyone having to establish templates before communication is a really stupid thing. These templates should be reusable. Secondly even if templates need to be created the standards should be unified.
That's exactly what MCP does. It's standardized communication between AI applications and external tools. cutting out tons of redundant development.
For instance, if a person figures out how to check the weather and share that codes with a community, the person B who also want weather info doesn't need to reinvent the wheel. They can just copy and paste it into their project. It's super time efficient.
Now that we understand what MCP actually does, let's check out how it works internally and what all this talk about host, client, and server really means. MCP host is basically your AI application. Think cloud desktop or AI powered coding tools like cursors or other ids that supports MCP.
The MCP client is code that lives inside these AI applications. It's essentially the messenger between your AI application and the MCP server. If your AI application already support MCP, then you usually don't need to do anything about it.
And the MCP server is the communication bridge between MCP client and the external tools. If you look on GitHub right now, you'll find tons of pre-built MCP servers for all sort of things like SQL data retrieval, data analysis, web scraping, machine learning, modeling, you name it. If your AI applications support the MCP protocol, you can plug it into this massive ecosystem of external tools with just a few lines of code.
The whole point of technical protocols is to boost the development efficiency by making collaboration smoother. MCP, which stand for model context protocol, is specifically designed to make developing AI applications way more efficient. To help get MCP more widely adopted, Anthropic has released a complete set of development SDKs for building MCP clients and servers.
These support Python, TypeScript, Java, and other languages. With these SDKs, you can whip up an MTP server with just a few lines of code and connect it to any MTP client to build a powerful AI agent. Of course, MTP is still quite new and comes with some growing pains since it is a recent innovation.
The numbers of tools with robust MTP support is still limited and you might encounter some bugs that requires debugging time. But the ecosystem is expanding rapidly. Just last month, OpenAI announced that their agent SDKs now supports MCP2, which is a huge step forward for broad adoptions.
And that's all I got for this video. If you like this video, feel free to leave a like and subscribe.
