MCP In 26 Minutes (Model Context Protocol)

I learned MCP for you. In the past 68 months, you've probably heard a lot of people talk about MCP. MCP is so amazing. MCP is such a game changer. Well, I must tell you, they are correct. I have taken a bunch of courses, build MCP servers and clients for our team and a couple B2B clients that we work with as well. We've also included a section on MCP in our AI agents boot camp. So, in this video, I'm going to explain to you the fundamentals of MCP, show you how to use MCP servers and how to build your own MCP servers as well, both with code and no code. Throughout this video, there will be little assessments, which if you can answer these questions, then congratulations, you'll be educated on MCP. Now, without further ado, let's go. A portion of this video is sponsored by Balt. Okay, a quick overview of the structure of today's video. First, we're

going to define MCP, talk about why it's important, and how it is that we use them. Then we'll be diving under the hood in which I'll cover the fundamentals of MCP, the concepts and frameworks that govern it. Basically, everything you need to know to be able to use and build your own servers. But it is not enough just to know all these concepts. So I'll actually show you how to build one as well, both with code and no code. Let us now define MCP. MCP

which is the abbreviation of model context protocol, is defined by Enthropic, the people who made it, as an open protocol that standardizes how your LM applications connect to and work with your tools and data sources. Okay. What? So first time I read that definition I was like sounds important. So let me actually explain it to you the way that I understood it using an analogy. MCP is like this USB plug here. It allows any device to be able to connect to any other devices work together. Prior to this universal standard of the USB port, if you wanted to connect things to each other, like say your computer to your microphone, to your webcam, you would be having a very bad time because all of the ports to connect with each other would be different, which means you would have to get a bunch of different special cords and different adapters to manage to get them to work together. And then you would have to write the software to cater towards like those specific port thingies, etc. Very complex, very messy. But with this USB port, we're able to now just connect all these devices to each other and they just work. Amazing. Wow. Do not be fooled. It sounds like such a simple little thing, right? Just like standardizing the port connectors, but it amounted to massive amounts of innovation with this ease of use. Now before MCP existed, if you have your agent and you wanted to give it access to different tools and different context or different types of data, you would have actually faced a very similar problem to the pre- USB port days because each external system and tool that you're trying to connect to has their own specific definition of how it is that you interact with it, which is their special API. For example, if you're trying to make a scheduling agent, you would need to give it access to like your calendar, your email, a note-taking app, Zoom, Calendarly, and you would need to custom write the code for each specific tool that you want to give your agent, which is a pain in the ass. So that's why when Enthropic proposed the model context protocol, the MCP, which would standardize the ways that we interact with all of these external softwares, everybody was like, amazing. Wow, so good. Now your agent is able to have access to all of these different external softwares and tools using the same protocol. Within just months that the number of tools that you can give your AI agents now, which are called MCP servers massively exploded to thousands. Now there's over 20,000 pre-built MCP servers that you can just grab and give your AI agent. Here is just a small sample. Now, anybody can easily write them, publish them, and use them. Let me actually show you exactly how easy it is to use an MCP server. Like say for example, I'm looking at these MCP servers here. You can pick any of them, but let's just pick like Alpha Vantage for example. It is a MCP server that enables LLMs and aentic workflows to seamlessly interact with realtime and historical stock market data through the model context protocol. Okay, great. So, you can get access to stock market data. You copy this stuff which allows your LM application to have access to the MCP server. copy it into your LM application like for example cloud code if you want to give the tool to cloud code and voila you'll be able to have access to the MCP server and all the different tools that are defined on the server then you can do something like plot the coffee stock market prices for the past 10 years then Claude will say Claude wants to use the coffee function from Alpha Vantage are we going to allow this and we'll be like okay allow once using that tool was able to get the coffee price data and now it's going to created interactive visualization and there you go, coffee global prices with all of this information. Oh my god, coffee is so expensive these days. So yes, this is pretty much how easy it is to use any MCP server now. And I can also very easily switch what is called the host which is cloud code in this case to something else like NA10 for example or my own custom AI app. So let's now actually dive into the foundations of MCP so you will exactly understand what

just happened here and how to build your own MCP servers as well. And by the way, MCP servers are actually even more powerful than just giving your agents tools. There's a lot more that you can actually do, which is why people are able to actually build these MCP servers and then sell them. The best course that I found which covers the foundations of MCP is Anthropic's own course in collaboration with deep learning. AI called MCP, build rich context AI apps with anthropic. The course starts with explaining basically why is MCP important. So I already covered most of that. Um but I do like the diagrams that they gave which shows that see before MCP there was fragmented AI development with each AI app you would need to have a custom implementation custom prompt logic custom tool calls and custom data access. And you got to do that for all of them versus with MCP which is a standardized AI development for each MCP compatible application you're able to have access to a lot of different types of MCP servers. For example, you can have access to a data store MCP server so that you can have access to data stores. You can have a CRM MCP server so that you can have access to CRM systems and a version control MCP server so you can have version control software. All plug-andplay and the other way around as well. Every time you build an MCP server, it would be reusable by various AI applications like a Google Drive MCP server that has access to Google Drive functions that can be used by MCP compatible AI assistant, MCP compatible AI agent, and an MCP compatible desktop app or IDE too. All right. Next important concept to understand is the client server architecture. Within the components that make up MCP, there are three different components. The host, client, and server, HCS for short. And the way that they relate to each other is the host houses the MCP client, which goes through the MCP protocol in order to have access to the MCP server. Hosts are LM applications that want access to certain data or tools through MCP. As I showed earlier, the cloud desktop is an example of a host. NA10 is also You can also have IDs, AI agents. Really any large language model application can be a host. Now the host wants to use the MCP servers which are the lightweight programs that expose different capabilities like the servers here like alpha advantage we talked about earlier that's able to get real-time historical stock data. Uh there's also time provides time and time zone conversion capabilities. Cherry studio that's used for LM provider support like AI assistance and conversions, documented data processing. Postgress SQL to provide readonly access to Postgress SQL databases allowing schema inspection and execution of readonly queries, GitLab, Reddist, etc., etc. There is a lot. Now, in order for the host to actually have access to these MCP servers, they need to maintain what is called an MCP client, which lives inside a host and evokes the MCP protocol to maintain that one-on-one connection with the server. Got it? MCP client lives in MCP host to invoke the MCP protocol in order to have access to the MCP servers which the players being HCS host client and server. All right, time for a little

quiz for the example that I showed earlier which allowed cloud desktop able to access historical stock data alpha vantage and cloud code. Which is the host and which is the server? Put your answer into the comments. Okay, let's now talk about what are the things that are actually contained within an MCP server. Well, there are three major

things. These are tools, resources, and prompt templates. TRP tools, which are the things that we've kind of seen the most of are functions and tools that can be invoked by the client. So, these are these things over here like time series, intraday, time series, daily, blah blah, symbol search, global quote. Also, stuff like sending a message in Gmail, calculator, retrieval and search, sending a message, updating database records. All of these are tools. But that is actually not all. You can also have what are called resources which are readonly data that is exposed by the server. So this is the data that the client is able to query but cannot change. For example, there are certain files like markdown notes that's contained within the server that you might want to read. Maybe it's tracking logs. Say for example, every time you're calling a weather app tool and you're getting that data, you might want to actually save a record of all the different weathers that you've collected. So at some point you might want to compile that together and make like a visualization or something, right? It would not be very efficient if every single time you had to go collect a weather data point. You would have to like use the tool and spam the tool over and over again. It could also contain just database records of stuff. Like for example, there's just like information that you're storing like contracts, meeting recordings, and notes. Then there's also prompt templates. These are structured prompt blueprints so that you don't have to force your user to have to come up with their own prompts. Like for example, if you are someone who's trying to use an MCP server that uh is able to summarize like a certain transcript from a meeting and then generate a report based upon it, you could just write something like, oh, summarize this transcript into key action items, then generate a report, right? Like you could write something like that, but that probably won't yield the best results. So instead, the people that actually built the MCP server can put within their MCP server a prompt template that is much more flushed out that's able to actually extract the insights and generate the report in a much better fashion so that the user all they have to do is input like some specifics into that template. This takes the burden of the prompt engineering from the user. As a full example, you can have like a SQLite MCP server which gives access to like a database which is the SQLite database. Don't worry if you don't know what that is. is basically just like a database that can store information. Anyways, so you can have tools that allow you to read the SQL database to insert information into the database to change the information in the database and to delete information from the database. In addition to that, there is a resource that is logging all of the changes that are happening to the database so you have a historical track record of what's happened. And there's also some prompt templates that incorporates the best practices to interact with that database. Make sense? All right, time for our next little assessment. Please answer the questions

on screen now and put it into the comments so you are following along. MCP is all about giving your agents more context so that they can do really powerful things. So when you're ready to deploy your AI agents with a lot more functionalities and you want really good UI, NA10 plus Bolt is a really powerful combination and you don't need to write a single line of code or you can just directly build via Bolt. Bolt can take you from an idea to a fully functional product within minutes. Whether that is software for your team, an internal tool or a website. It has everything that you need, whether that's hosting, backend, database, off, domains, and even SEO. One of the things I really like about Bald is that it seamlessly handles the entire process so that you can focus on building real things as opposed to handling infrastructure. You don't need to handle like setup, configs, war, external tools. Bolt puts the most powerful tools used by developers into your hands with an intuitive chat interface. It really is as simple as this. So, here we have the Bolt new landing page. And we can just type something like build an app that blurs out people's faces in pictures and just click build now and then let it do its thing. Of course, you can go into a lot more details and have a lot of like specific specs within the app as well. And look at that. Here we have this application and let's actually try it out. We will put an image. Here is a picture of me when I was still a little Tina. And there you go. We have the original and then we have the blurred version. And you can download the blurred image as well. As well as you keep building, you can include a lot more features and tweak things however it is that you want. Let Bald do the heavy lifting so you can focus on your vision instead of fighting bugs. You can try out Bald V2 for free today at this link over here, also linked in description. Now, back to the video. Okay, so the final concept that I want to cover for MCP before diving into actually building them is the

communication life cycle. The way that the MCP client and the MCP server communicate with each other. The first step is the initialization when the MCP client interacts with the MCP server being like, hey, I would like things from you now and they initiate a connection. Then you have the message exchange phase when the MCP client makes a request to the server. for example like I want to use this tool and the MCP server goes like okay and gives it back which is the response. So you do that exchange the messages and then finally there's the termination phase when the client and the server terminate and stop interacting with each other. Now the way that the messages are done is called a transport. The transport handles the underlying mechanics of how messages are sent and received between the client and the server. And there are two different categories of how transports work. First one is when the server is running locally like your MCP server and your host all of this is within a single machine. The analogy that I like to give here is say that your MCP client is like the diner and the chef is like your MCP server who is going to be the one preparing the food for your diner. Now for the server and the client running locally, it'll be like just you and your friend cooking at home with like a notebook. You as the chef is just like cooking it and maybe you're just like writing down what it is that you're doing and like just passing a note to your friend the diner for some reason. So they're like keeping updated about what's happening while you're cooking. That is like the standard out a running log that's just like living locally between you guys. You don't need to rely on like a waiter or a cashier or anything like that. The other category of transports is for remote servers. So these are servers that live in other machines on the cloud somewhere else. And there are two different approaches to interact with a remote server. The first one is called HTTP plus SSE which is server sent events. Don't worry so much about the terminologies. It supports what is called a stateful connection. Going back to that analogy, in this case, you would have to like leave your house and say you're at a restaurant. You, as a diner, would sit down at the restaurant. You're the client, and you would interact with a waiter, which is a server, and you would tell it stuff like, "Oh, here's my table. Here's the appetizers that I want to order, blah, blah. " And then your server would like bring you those things, and say if you want to order like the same drink again, you could just say something like, "I'll like to have the same drink again. " And you don't have to like exactly specify whatever it is that you ordered the first time. So this is called a stateful connection because the server maintains the state of it like remembering the knowledge the memory of what you ordered all this information throughout the entire interaction. Now there's also what is called stateless stateful stateless. This kind of transport supported by a streamable HTTP. To be stateless is sort of like if you go to a fast food restaurant as opposed to like a sitdown restaurant. Like if you're at McDonald's for example, you would order like a Big Mac and I don't know like fries. But then like if you want to get something else, you want to also get like chicken nuggets, you would actually have to go and like input your order all over again and get like a new ticket and you have to wait again. The cashier that's clashing you out, the server doesn't actually remember everything that you ordered previously. So that's why it's called stateless because it doesn't remember all of the knowledge that came with your prior interactions. Each request is independent of each other. So without going into way too much detail about this like the streamable HTTP is the preferred transport method because it's able to support both stateful and stateless and there are instances in which you want to have like stateful interactions and stateless interactions as well. So this section might sound a little bit like detailed and theoretical but it does become very important when we're actually going to be using our MCP servers and building our MCP servers which is right after I give you this final little assessment.

Okay, I'm going to show you the no code implementation of building your very own

MCP server now. And we're going to be doing this by using n10. So you can do stuff like send an email to Tina with the address tina@ lonelyotopus. com asking her if she wants to grab a coffee sometime. Execute workflow. Press enter. goes through this. We can see that it's accessing the AI agent is accessing the MCP client which is here. It contains the ability to have a calculator and also the tool that allows to send a message to Gmail. So, it's able to access the tool to send a message to Gmail so that you will be able to get this email. Hi Tina, I hope this message finds you well. I was wondering if you like to grab a coffee sometime. Let me know what works for you. Best regards. Email was automatically sent with NATO. Okay. So, how to build this from scratch? start off with a fresh new workflow using NA10. If you have no idea what NA10 is and you're not familiar with it at all, check out this video over here. I go into details about how to build AI agents using NA10. I'm not going to go into this here, so the video is not super long, but basically it's a no code low code tool that allows you to build workflows. Also MCP servers. So you have your server trigger over here and you want to add some tools. Like for example, we can add the calculator tool, which is just like the ability to calculate things and do math. Not super innovative but yes. And then we can also add the email tool. So maybe like a Gmail tool. And in here we need to set up the credentials like create new credential. You can kind of go through the process. So all you have to do is like sign into your Gmail um which I will do here. And then you'll have the account that is connected. Yay. Then um because we just want to send emails here. So the operation here is going to be send. So for two we're going to let the model decide these. So, the subject. Let's al also let the model decide uh we'll have the email type HTML and the message. Let's also let the model decide that as well. Wonderful. Amazing. So simple. Right now, let's save this. Now, in order to actually use our server, we want to come here, make sure that it's active. So, the server itself is going to be active. And we want to take this production URL. So, this allows access to the server. Now I have another workflow over here which is an AI agent in which you can prompt it directly and respond also attach the open AI chat model here. So in order to add the MCP I can click the MCP client tool the endpoint we're going to copy paste what we got from the MCP server and here in the server transport see we have the server send events which is deprecated and the HTTP streamable the two different transport right so we're going to click HTTP streamable transport and we don't need to change anything else over here. Wow. Now we have the MCP client. So let's actually test it out. Let's actually do math this time. Calculate 10 11 * 99. We can see here that the AI agent goes over here. Human says calculate 11 * 99. It goes to the MCP client and it uses the tool cal the calculator tool which we have. And it says useful for getting the results of a math expression. And then the open AI chat model was able to get the results by using the MCP client. And then we were able to get the final result here with the output being the result of 11 multipi by 99 is 1,089. Yay. See how easy it is. There's a lot that you can do with this. Of course, you can add like so many more tools into your MCP server. And you also don't need to have NAN as your host. We can easily switch out the host for something else. Like for example, maybe you want to change out the host for cloud desktop. So what we need to do is go on cloud desktop. So go on help, type in developer. So switch to developer mode and then under developer open the claw desktop config file. So this should be empty. You can open it with whatever text editor. So I'm here I'm just using VS code for it. And you want to copy paste this. So this is defined MCP server and we're using NAN because that's where the server is and we are using SSE over here. And over here just paste the endpoint here. So command S or control S. Save it. Restart Claude and wow amazing. We have NA10 over here under the tools and includes send a message in Gmail and the calculator. And then you can do the same thing here. Send an email to Tina@ lonely octopus. com asking her to host an octopus party. Click enter. See it'll say over here Claude wants to use send a message in Gmail from NA10. And you can click allow once to allow it to use the tool from the MCP server. And moment of truth, we have over here, "Hi Tina, I hope this message finds you well. I want to reach out to see if you'd be interested in hosting an octopus party. I think it'll be a wonderful event and you'll be the perfect host for it. Let me know if you're available and interested. Looking forward to hearing from you. Best regards. Yes, automatically sent with NAT. " Voila. See, super easy, right? Okay. So, now I want to show you the

code implementation of doing this. Now with code you are able to do a lot more than this no code approach. I think the no code approach is amazing. It's great, right? It's really accessible and easy to use. Remember we talked about earlier about having like additional resources and also prompt templates. You as of right now cannot do that using NAT. You can't actually put in the prompt templates and specify those and you also can't put in additional resources as well. And in the code implementation you can. So I'm not going to go and show you every single line of code right now because I don't want to make this video like super duper long. But I want to show you like the snippet of these are the tools that we're going to be using and this is how you can actually get it to run. Okay. So for this code demo, we're going to be using cloud desktop as the host and I'm going to showcase a simple MCP server that's able to provide tools, resources, and prompts for working with Google Sheets and Google forms. So we can tell that the MC3 server is connected because if you go to G Sheets over here, which is the MCP server name, you can see all the tools that are contained over here. So I have a lot of different spreadsheets that are available. So let's actually ask list out the spreadsheets that are available. It will want to use the list spreadsheets. So this is the tool and it tells us that we have 20 spreadsheets that are available in the Google account like NA10 form, anonymous feedback, the only octis alumni blah blah etc. All of these different ones. Would you like me to read the data from any of these spreadsheets here? I can say use read sheet to get the columns of the anonymous feedback spreadsheet. So it wants to use read sheet and this is actually a resource. So it allows you it's a readonly file where you can actually read what the column names are. You can see the spreadsheet ID and the information that's contained over there. And finally, I want to show you what it looks like to use some of the prompt templates. So, if we click over here, add from sheets, here are some of the prompt templates. So, let's click analyze sheet data. And we can say use this to analyze the anonymous pretty spell anonymous feedback spreadsheet and create a dashboard just to make it look pretty. And we can see here is what the prompt template actually looks like. And it shall do this. Let it do its thing. So while it's doing that, let me actually show you what the code looks like and how to run this MCP server. Here's the readme. If you follow these instructions, you'll be able to run this MCP server yourself. You can see that the tools that it contains include things like list spreadsheet, read the sheet, write the sheet, append the sheet, etc. It also has the resource where you can use the sheet in order to access sheet data as the resource. And it has prompt templates including analyze sheet data which is template for analyzing sheet data. Creating report template. So a template for creating report spreadsheets and form to sheet which is a template for form to sheet workflow in the code itself. Not going into too much detail over here. You can see that after we define the credentials the way that you can define these tools that you can decorate the function. So you know that's MCP tool. So this one is the function for listing the spreadsheet. This one is for reading the sheet etc. And then the way that you can show that it's a resource is that you just decorate it so that you know that it's a resource. So get sheet resource. And here are the prompt templates themselves. So they're actually hardcoded directly in these different prompt templates. So let's go back to claude here and see what is happening there. Great. We see that it analyzed the anonymous feedback spreadsheet. So I read that information, analyzed the data using the template that we provided and it actually created a dashboard as well. So here is the dashboard. So total response is 43. The average MPS score which is the net promoter score is 4. 9 5. 98 blah blah all these things. So it tells you information about this different categories uh how the study plan progress tracker is qualitative information about what attracted students to our boot camp and insights as well. So different types of information including what works well and what doesn't work well etc etc. So yeah there you go. This is the code demo of an MCP server. I hope you can see that there's so much potential for so many different types of things that you can build now that you have the ability to create your own MCP servers. You can of course also change your host. You use this MCP server with NA10 uh with your own agent with your any large language model application that you build as well. The possibilities are endless. So if you do want to dig deeper into this, I highly recommend that you actually check out the anthropic deep learning AI course that goes into the exact implementation step by step how to build this entire MCP server. It is really, really powerful. There are of course a lot of other ways that you can implement MCP servers as well, both code and no code approaches. I also really like this one by Network Chuck where he goes into a lot of detail about how to use Docker in order to do this. So I'm going to link his video below as well. I really recommend that you check it out if you're interested in implementing it using Docker. But for this video, I'm going to end it here. I hope that you now have an excellent fundamental understanding of MCP and how to actually build them yourself, too. Here is the final little assessment. Please answer these questions and put them into the

comments. Hope you enjoyed this video and thank you so much for staying until the end. I'll see you guys in the next video for live stream.