Now it's time to start coding. In this lesson, you'll build a chatbot and code its tools. Before you start working with MCP, let's make sure you have a good foundation with tools use and prompting large language models. All right, let's code. So let's get started building our chatbot. And before we hop into building MCP servers, we're going to start just by building a very simple application with a chatbot that makes use of archive for searching for papers and finding some information. If this is information that you're already familiar with, feel free to skip this lesson and hop into building your first MCP server. So let's get started by bringing in some libraries that we need. So we're going to bring in the arxiv SDK. This is going to allow us to start searching for papers. We're then going to bring in the JSON module for some formatting the OS module for environment variables. We're going to bring in a little bit of typing so we can type our code. And then we'll make sure we bring in the Anthropic SDK. I'm going to first start by defining a constant here called paper directory, which is just going to be the string papers. And this is what I'm going to be using for saving information to the file system. I'm then going to bring in my first function here. So let's go take a look at this. This function is called search papers. And it accepts some kind of topic and a number of results which defaults to five. And what I'm doing here is searching for papers on archive. And if you're not familiar with Archive is an open source repository of many different published papers across a variety of domains, from mathematics to science to many different disciplines. So we're going to search for some papers, and then we're going to return a list of paper IDs. And we're going to then use those paper IDs in another function to get more detail and summarization. We're going to initialize our client. And then we'll start searching for relevant articles. We'll take the results of those search and we'll go ahead and create a directory if it exists already, great. If not, we'll go ahead and make it. And we'll save this information to a file called papers info dot JSON. What we're going to do is process each of these papers and create a dictionary. And then we're going to go ahead and write that to our file. This is going to give us back some IDs when we're done. Let's go ahead and search for some papers for computers. We'll see here. This is being saved to a file locally. And we got a bunch of IDs that we can use to get some more information around. We're going to go ahead and bring in our second function here now to make use of this paper ID. So we're going to define another function here called extract info. Which is going to take in one of these paper IDs it's going to look in our papers info JSON and give us back some information about that paper. And if it can't be found we'll go ahead and just return a string. There's no saved information for that paper. So I'm going to go ahead and grab this first ID right here. Let's go ahead and run this function. And then we'll go ahead and call this function with that particular ID just to show you what this looks like. We can see right here we're getting back some data not only related to the title of this, but also the URL for the PDF as well as a summary of this particular paper. We're going to start with these two functions. And then we're going to go ahead and start bringing in these functions as tools for a large language model. So what we're going to be able to do is pass in some tools for Anthropic's Claude model. We're then going to go ahead and build a small chatbot that takes in these tools and knows when to call them and return the data, particularly for these functions. So let's define our tools list. If you are familiar with tool use, this should be nothing terribly new, but every single tool that you make is always going to have a name and a description and then some kind of schema that it needs to follow. So in this case, we have a tool for search papers and a tool for extract info. Remember that the model itself is not going to call these functions. We actually need to write the code to call those functions and pass the data back to the model. But these tools are going to allow the model to extend its functionality. So instead of saying I don't know or hallucinate, we're going to get back the answer that we want here. So let's go ahead and start writing some of our logic for working with our large language model and executing these tools. Let's bring in a little bit of mapping for our tool. And right here we've got a function that is going to map the tools that we have to calling that underlying function. What you can see here is we have a dictionary for each of our tool names. These refer to the functions that we have below. And then a handy helper function to then go ahead and call that particular function and return the result to us in a variety of data types that come in. Let's go ahead and start building in our chatbot right now. That's going to start by bringing in any environment variables that we have API keys, and then creating an instance of our Anthropic client. We're going to need this so that we can go ahead and make calls to our model and get back some data. Let's go ahead and bring in our boilerplate function to go ahead and start working with the model. If you've worked with Anthropic before or many other models, this is going to look very familiar. We're going to start with a list of messages. We're going to go ahead and pass in the query that the user puts in. I'm going to talk to Claude 3.7 Sonnet right now. We're going to go ahead and start a loop for the chat. And if there is text data put that into the message. If the data that's coming in is tool use, if the model detects that a tool needs to be used, we're going to go ahead and bring in our helper function for executing the tool and then appending that tool result to the list of messages. Let's go ahead and see this in action. Bring in our chat loop. We've got all the functionality we need to start working with tool use talking to our model. Now let's start with a very simple example for what it's going to look like to actually use this function. We're going to run an infinite loop until we pass in the string quit. So let's go ahead and start talking to our large language model. Call our chat loop function. Right now we can start putting in a query to start talking to our model. So let's start with something very simple, like just saying hi and make sure this works as expected. We can see the model here is going to let us know not only that it's an AI assistant, but also let us know some of the tools that it has available. This is excellent. So let's go ahead and start making use of these tools. Let's search for recent papers on a topic of interest. So why don't we go and search for papers on algebra. And this should make use of the tool that we have to go ahead and search for papers. We can see that topics being passed in and the results are saved here. This is great. It's even following up with would you like me to extract more detailed information. So I'll go ahead and say yes please extract information on the first two you found and summarize them both for me. So what we're going to do is make use of that tool result that we got before. And we're going to pass that in and it's going to tell us which IDs it's interested in. So I'm going to make sure that I pass in those particular IDs so I can get that correctly done. The IDs are here. So we're going to see here it's going to extract the info with these particular IDs. And we're going to go ahead and get the result as well as a summarization here. So we got some information about in variant algebras and deformation of algebras. Honestly, I cannot tell you what this is. But hopefully I can read the paper and get up to speed on what this information has. Something to remember is that there is no persistent memory here, so as you go ahead and search for queries and get IDs, nothing is going to be stored permanently. So just make sure as you keep querying, you're passing in those IDs and think of each conversation as brand new each time. If you ever want to get out of this chat, remember we can always type in quit. So make sure you run that and we'll see that we're all done here. So what we've seen in this lesson is a review of large language models, tool use and making use of the archive SDK. What we're going to see shortly is how we can refactor this code to turn those tools into MCP tools to allow for a server to pass that information to us. We're then going to test that server and see what the results look like. I'll see you in the next lesson.