Welcome back to the course.
In the first part of this lesson, you learned how to build a WhatsApp AI agent that can process and
analyze images for you, respond with voice messages, and of course, handle text conversations.
So effectively it can speak written.
See, in this part we are going to build an MVP server from scratch and attach multiple tools to it.
So then when we connect our WhatsApp AI agent we created in the first part of this lesson, the agent
will be able to manage your to do list, read emails, and create email drafts.
I'm also going to show you how to call sub workflows through your MVP server setup to generate social
media posts.
In case you didn't see the first lesson about MVP in this course, I will introduce you to this concept
so you better understand how it works and why it's so amazing.
And even if you watch, uh, that lesson, this will be a good reminder and make it easier for you to
build with me in this lesson.
Also, I will teach you how to connect to your MCP server to AI tools such as cloud desktop app.
Or cursor.
So you will be able to use these tools directly from those applications.
So let's get started.
According to anthropic, the company behind cloud, one of the best large language models on the market
right now.
MCP was launched in November last year, but only recently started to gain real traction.
So basically, MCP is a standardized way for large language models like cloud or ChatGPT to connect
seamlessly to all your favorite apps and services like Gmail, CRM, calendars, databases, rack agents,
and more.
And MCP gives LMS access to tools and data and lets them choose what to use based on the context of
your prompt.
As the name model Context protocol suggests, a great analogy is this MCP is an open protocol that standardizes
how applications provide context to LMS.
So think of MCP like a USB-C port for AI applications.
Just as USB-C provides a standardized way to connect your devices to different accessories, MCP provides
a standardized way to connect a models to different data sources and tools.
This diagram shows how MCP acts as the layer between your agent and everything it needs to work with,
like tools, data, and user input.
So on the left you've got tools like search.
Search GitHub.
So this can be triggered automatically by the agent in the middle.
There are resources like PDFs or user profiles which provide context.
And on the right prompts they let users guide the agent with templates like summarize or check compliance.
So all of this is processed by your LLM or agent.
And the MCP layer sits just benefit making sure the agent connects to everything properly and consistently.
So then finally the agent can take action using APIs and microservices, databases or team tools or
through this one unified interface.
So instead of building custom code for every integration, MCP acts as a universal translator for your
agents.
Now let's dive in and see how you can use it with N810.
Recently.
At the beginning of April, the Ne10 team released native MCP notes, which means we now have dedicated
built in support for working with MCP.
Before that, the only way to use MCP in A-10 was to self-host an A-10 and manually add the MCP server
as a community node from GitHub.
So it worked, but it was definitely more technical.
Now it's much easier and I will show you step by step how to get started using this new native node
in your workflows.
So yes, in this tutorial I'm going to walk you through step by step how to set up the MCP server in
A-10 and what it can do, and how you can connect to it from tools like cloud, desktop app, or even
another workflow using the MCP client node.
The MCP server trigger listens for incoming requests from tools like cloud Gmail to send emails.
Google calendar to create events or calculator.
Just a simple example tool.
So these are all tools our agent can access and control.
Now on the other side you got the MCP client.
So in this setup the agent starts when a chat message is received.
So for example a user saying add a meeting to my calendar.
So you can ask the agent to do that for us.
And then it passes the request to the agent host.
And the MCP client node here gives the agent access to all the tools available on the server side.
So all those tools.
It also remembers what tools are allowed and manages how the agent communicates with them.
So the server side handles the tools and the client side is where your LLM is.
And we call that the host.
So in our example here, the agent is a host because it connects to the MCP server to call the tools.
So we'll also be using the cloud desktop app as another host.
And I will show you just how cool that setup is in action.
Now let me give you a quick look at what this setup can actually do.
So again on the left side of my workflow I've got the MCP server.
It has access to a few tools.
Now let's say I jump into the cloud desktop app.
Which acts as a as a host in this case.
In our second example, the agent acts as a host.
But for this demo we are using the cloud desktop app.
So when it connects to my MCP server Through this node, it instantly sees all the tools I've given
access to.
So now in the cloud desktop app, I can ask it something like send email to Damien at n810 demos at
gmail.com.
That his agents course is amazing.
Use gmail tool and when I press this button to start this task.
You can see that cloud processes the requests.
I'm going to give it permission to to do this task for me and use the Gmail tool we added in our N810
setup to allow for this chat.
Here you can see how the setup uses the Gmail GMAT tool to send the email.
The email has been successfully sent to Damien at n810 demos at gmail.com, which with the subject your
agents course is amazing.
Now let's jump into my gmail inbox and check if I got the email.
Let's refresh it.
And as you can see, I got an email.
The subject agents course is amazing.
Hi Damien, I wanted to let you know that your agents course is amazing.
The content has been incredibly helpful and informative.
Thank you for creating such valuable resources.
Best regards.
So it works.
And now what actually happened here?
Let's go back to N810 setup.
So.
So MCP stands for Model context protocol.
So think of it as a bridge.
So you set up a central server.
So in our example the MCP server with tools and workflows you want to share.
So then from outside other apps in our example we are using the cloud desktop app.
Or agents can connect through the MCP client node and instantly use these tools.
So no need to manually add every tool to every agent.
And it's super efficient, especially if you are managing a lot of workflows or building AI systems.
So again, we connected the cloud desktop app to our MCP server trigger and large language model.
So in that case is able to use these tools to perform specific actions.
And we wanted it to send an email to myself.
But this example is just for the sake of this tutorial.
So it's very simple, but you can add a lot of more tools to this setup and give it more complex tasks
to do.
Yes, when you build an MCP server, you can plug in into different apps that work with MCP like cloud
desktop app or coding tools like cursor.
So look at Cloud Desktop here.
I can see when I just click on this and then.
I can see six different tools available.
So these are all the tools I connected to my MCP server through N810.
Things like adding tasks, creating content, handling emails, showing my task list and updating tasks.
So all of them, they come from this workflow.
And what this means is closed can now use any of these tools.
So I can ask for example show me.
My current.
Tasks.
And let's say.
All right.
Let's keep it simple.
I'm going to send it.
And because my workflow is active cloud sees these tools are available and asks if I want it to run
the task list tool from any ten.
So I approve it.
All right.
And now it shows all my tasks.
So these tasks come from.
This tool right here.
Which connects to my Airtable database.
Let's have a look.
As you can see, gym is completed shopping and recording a new tutorial and not completed.
Let's double check.
Pending tasks.
Recording and tutorial.
Shopping.
One task without a name and complete tasks.
You have three tasks that still need to be completed.
Would you like me to help you update any of these tasks or add new ones?
Yes, it's correct because the gym is marked done, but I still have to do shopping and recording tutorial.
They are not finished yet so let me update these tasks.
I will say.
Add a new task.
Called call.
Dentist.
And also add mark.
Shopping.
As done.
Right crowd response was interrupted.
This can be caused by network problems or exceeding.
Okay.
Let's try again.
I think there is some network problem because I've got a pro subscription so I shouldn't run out of
tokens.
Let's try again.
But this time let's use.
Cloud sonnet 3.7.
So I'm going to ask it to add a new task.
Called call dentist.
And.
Mark.
Shopping.
Dome.
Let's see.
Hopefully it will work.
Yes.
Great.
So plot is using our art task tool.
So this one.
And are you approve it?
Let's wait a moment.
Now the cloud is processing the request.
It shouldn't take so long.
I think there is some network issue.
Here.
So I cut to start the chat.
Can I go to approve it again?
Now let me update it.
So now it wants to update the existing task.
I will approve this too.
Now let me update the shopping task to market is completed.
I will add a new task called dentist and mark the shopping task as completed.
Let me do that for you right away.
First, let me add a new task.
As you can see, the task has been added.
Now let me update the shopping task to mark it as completed.
Okay, just using updates to the tool.
Both tasks have been updated Objective successfully added a new task called dentist, marked shopping
as completed.
Is there anything else you would like me to do with your task list?
All right.
Perfect.
So close.
Says it's finished.
And now if I check my table.
As you can see, I don't need to refresh it.
And shopping is now marked complete and dentist got added to my list.
Perfect.
Now our MCP server also handles emails so I can tell cloud something like.
That my task list today, man.
At example.com.
Alright, so it's getting the task list first.
I'm going to approve it.
I will compose and send the email.
So now it's asking permission to send the email.
So I will allow that.
I will send your current task list to example.com.
Again let me fetch the latest task list first.
And here you can see using show todo show todos tool.
Now I'll compose and send the email.
And for sending an email.
It's using the Send Emails tool.
So this one.
I've sent you an updated task list to the example.com.
The email includes both your pending tasks and completed ones.
The image has been successfully delivered with the subject.
Current task list.
June 19th, 2025.
Is there anything else you'd like me to help you with today?
All right, let's check, because cloud says it's done.
When I check my inbox now.
As you can see.
I got an email sent to.
Example.com, which is correct.
And the message is.
Hello, Damon.
Here is the current task list.
To complete recording tutorial call dentist name task complete to name, task and complete the tasks.
Gym.
Shopping.
Let me know if you need any other information.
Best regards.
And of course I got this address not found message because Damian at example.com doesn't exist.
All right great.
Let's move on.
Now, before I show you how to use the same tools, we got connected to our MCP in cursor.
Let me briefly explain what it is.
So cursor is an AI powered code editor that's built on top of Visual Studio Code.
So it's designed specifically for developers who want AI.
Assistance while coding.
So you can chat with AI directly in the editor and get code suggestions.
And now with MCP support, you can connect external tools just like we did with Cloud Desktop.
So essentially it's like, um, it's like having AI Code Assistant that can also access all your custom
workflows and tools.
So it's designed specifically for what's called vibe coding, and it's becoming incredibly popular right
now.
So let me actually show you what vibe coding is by searching for it.
So according to AI overview, vibe coding is a term popularized by Andrej Karpathy, referring to the
practice of using large language models to generate code from natural language descriptions is essentially
a way to interact with code using prompts, with the AI handling the more technical aspects of translating
ideas into executable code, where you describe what you want in natural language and AI writes the
code for you.
And instead of manually coding, you just tell AI your instructions intentions and it handles the technical
implementation.
So let me dig deeper into this and I'm going to open perplexity.
And let me ask um I'm going to ask how to.
Cast coding.
Come.
In 2025.
And what are the main tools?
People are using?
Let's see.
So in 2025, pipe coding has become a rapidly growing and influential trend in software development.
Though it remains somewhat debated among developers.
Related adoption and main tools used in web coding in 2025.
And as you can see, cursor is one of them.
It's the first code editor with real time suggestions developer seeking enhanced coding productivity.
We also have replit.
Lovable.
Bold.
Windsurf.
It's also very popular.
All right.
So now let me show you Google Trends to see how popular this term has become.
You can see the massive spike starting in February 2025 when the term was coined.
This shows you how quickly this concept has taken off.
And that's exactly.
What cursor allows you to do.
So this WIP coding approach where you can build software just by describing what you want.
So WIP coding is essentially a way of programming.
As an engineer with nearly 15 years of programming experience, I believe that coding is already very
powerful because you can build tools, systems, and even MVP's without programming experience.
Of course, if you have a technical background, you can build more sophisticated applications and do
it much faster.
So web coding is becoming more and more popular.
And I strongly recommend you learning about it.
This course is not about cursor and coding and coding specifically.
I just wanted to mention it so you understand the context.
So when you are learning about MCP, MCP servers in N810 and MCP tools to platforms like cloud, desktop
app or cursor, we're actually building the foundation that makes this AI tools even more powerful.
So instead of just generating code, I can now manage workflows, handle your emails, update databases,
and interact with all your business applications.
So now when I open up the cursor app, I can see that MCP server detected N810.
So I'm going to enable it.
Great.
And now when I go to cursor.
Settings.
Cursor settings.
And the tools and integrations I can see that my cursor application is connected to MCP tools and ten
tools enabled.
So let's check.
So here in cursor I see update tasks add tasks tasks.
So basically all the tools are included and connected to my MCP server.
I've just added a new tool to create email drafts.
So let's test it.
It's going to rename it.
Draft.
Five.
Let's go back to Curacao.
Now let's open chat and let's give it a task.
So let's try.
Write a funny story.
About.
A pizza slice.
That becomes a superhero.
And saves the day.
Then put it in.
An in a draft.
As you can see, cursor is working now.
I just got the notification and the application is calling our create draft tool now.
So I will run this.
So because it's waiting for approval.
So run tool confirm I've written a funny story about pizza slice that becomes a superhero and saves
the day.
And I've placed it in an image just for you.
I would like if you'd like to review or send the draft, just let me know.
Here you can see the actual story, the subject and message.
All right.
Great.
Now let's check my Gmail.
Now when I refresh it.
I've got a new draft here.
The amazing adventures of Super slice.
Let's open it.
Once upon a time, in the bustling city of cheddar, while they lived a humble pizza slice named Tony.
Alright, great.
Let's go back to our, um, cursor.
So, as you can see, everything is working as expected.
Now let's go to N810.
Let's check the executions.
So click on the executions tab.
Perfect.
Here you can see that cursor was using the trigger to call this tool to to create the draft for us.
And that's really amazing stuff.
Now I'm going to show you how to build this MCP server from scratch and how to create all these tools.
And then we'll connect the WhatsApp AI agent we built in the first part of this lesson, so you can
use them via WhatsApp.
Finally, we'll integrate the MCP server and learn how to take advantage of it using cloud, desktop
app and cursor.
So you'll be able to use all these tools directly from those applications to get started with an MCP
server.
Just create a new workflow.
And I'm going to name this.
MCP.
All right.
So first thing I need to do is to search this search MCP.
Since the team released native integration we can just simply search for MCP.
MCP server trigger.
If you expose any tools as an MCP server endpoint.
And now when I look at this node, I can see that it gives me both test and production error.
There is also an option to add authentication.
I could use better token or authentication, but for this demo I will keep it simple with no authentication.
Now let's update the path to tutorial demo.
All right great.
I will give this URL shortly.
Now let's add tools.
Now any tools I connect to this MCP server trigger node will show up in whatever app connects to our
server.
So now I want to set up something really useful a to do list.
So let's add these three nodes.
So these three tools.
So first I'm going to create an Airtable database.
Now if for any reason you are not familiar with setting up a table in an A-10, even though we covered
it many times in this course, please refer to the dedicated video in the How to Set up Credentials
section and also the human in the loop tutorial, where I provide more details.
Now I will create a new table in this workspace.
So in my workspace.
So first let's create a base set from scratch.
Let's name it MCP.
WhatsApp.
Agents.
Tutorial.
So this is the name of our base.
I'm going to create an Airtable database to store our to do list.
So we need one table with two fields.
So let's rename this table.
Todos.
The first field will be task.
And we can leave.
This as a single line text.
Save.
Let's remove nodes.
We don't need all of them.
All right.
Now let's add a new column.
So add field.
Let's search for checkbox.
And let's name it.
Create field.
So our agent will be able to track if it's finished.
So let's add few items.
The first one game.
Topping book.
All right.
And let's mark them as completed.
As done.
Perfect.
Now I want to connect this to our MCP server so I can manage this to dos through our agent.
So back in our workflow.
All right.
So first tool.
Let's search for a table.
A table tool.
Now make sure your table is connected so it will show.
The table we just created will appear on the table.
So I'm going to call this one.
List to dos.
Right through description set automatically.
We can leave it as it is.
As a resource we want to record not based.
For depression.
I'm going to select search.
So we'll search for specific records or lists.
All right.
So for the base.
I'm going to choose the base I've just created.
So to do list.
For the table we want to choose from list as well and choose the table I just created.
All right.
Perfect.
So basically we want to fetch all the records from our table.
So I need to select my to do uh, base and table.
I will leave filters empty since I want everything I'm going to return all items.
And for options let's select the adoption and output fields.
And let's select the name as well as completed.
All right.
Great.
Now I will create two more table nodes for updating our to do list and also for creating a new record.
So first let's add a new tool.
Search for a table.
Let's leave the description as it is so set automatically preserves as record.
But let's change the operation to create.
Let's select our table.
Sorry our base.
So to do list and our table to do let's map each column manually.
Then the to do name will be determined by the.
I just simply click on this button here to let the model define the parameter.
All right.
Perfect.
And and I will set a completed to false since new todos start as incomplete.
All right.
Great.
Now I will add one final Airtable note.
So let's add Airtable as a tool.
Now I will call this.
Update to do.
This operation we want to change it to update.
Select the base.
As well as the table.
It does.
So this tool will change the status of existing todos.
So for the fields to update, I will have the.
I figured out what to change.
So I'm going to add a helpful description and explaining that it needs the Airtable record ID.
So this usually starts with Rec followed by random characters.
So this gives the AI model some guidance on the expected format.
Now for both the name and completion.
I will set this to be determined.
So it can handle different update scenarios.
Perfect.
Let's hit save.
At this stage I'm going to show you how to add an MCP server to the desktop app.
So we'll be building while developing the rest of the workflow for the rest of the tools.
So in order to get Cloud Desktop app, head over to.
Load Dot I slash downloads and grab the version for your operating system.
Currently I'm using Mac, so I'm going to choose Mac OS.
Then install the application.
Then login to your account or create an account.
If you don't have one yet.
Then go to.
Settings.
And you need to enable developer mode.
So you should see an option to enable developer mode.
So click on that.
Then navigate to developer mode.
Right now you won't see any MCP servers listed here because you need to edit configuration file.
So click on Edit Config at the bottom.
And this will open the folder.
If you close desktop configuration file.
So open this file using any text editor you prefer.
I will just use cloth itself, but you can use whatever you like.
For example, you can use Sublime Text or Visual Studio Code.
Now in this file you need to add some configuration code, and I will put all of this in the resources
section of this lesson.
Now here is something important.
The team recently changed their MCP configuration format.
So they moved from using a super gateway to a new MCP remote approach.
So the old format used to look like.
Let me show you.
Got it right here.
So the old format used to look like this.
With super gateway and webhook URLs, but now it uses MCP remote with proper authentication tokens.
And you look it looks like this.
So this new format is more secure and reliable.
Now what this means and this is really important if you are using a Cloud version.
You still need to use the old connection, because if you check the URL from your MCP trigger node.
You will notice that at the end there is SSE, which means it's using server sent events.
So you need the old super gateway configuration, but you need to install NodeJS, npm and npx packages
because as you can see in both connections they use the npx command and Npx runs the super gateway package
in the old format and the MCP remote package in the new format.
So if you are using the new self-hosted version or the latest N810 setup, then you can use the new
MCP remote format.
You should already have Node.js installed since it's required to run N810.
By using the wrong format, you will not be able to connect N810 to Cloud Desktop app, and I'm going
to provide detailed instructions on how to install Node.js, npm and npm in the resources section of
this lesson.
So to clarify, if you are using N810 cloud paid version in order to connect your N810 instance to Cloud
desktop app, you need to use the old code snippet, but you have to install Node.js and required packages.
But if you are self-hosting N810, you can use this new connection.
And you can find this configuration in the official documentation.
I'm going to include the correct code for both scenarios and all the links in the resources section
of this lesson, so you can easily use it.
Now back in our workflow let's open mic server node.
Let's switch to production URL and copy this URL.
Now again let's go to cloud desktop app.
Developer edit config.
Open the file and replace this URL with the URL you just copied.
Save the file.
When you are done, go to an A-10.
And make sure to activate your workflow.
Just simply make it active.
Toggle it.
Got it?
All right.
Perfect.
The next time you restart your desktop, you should see the tools icon appear.
So let's test it.
When you open the configuration file for the first time, it will look like this.
So simply remove the code and paste this code snippet I provide.
If you want to keep authorization, you can leave it and provide the bearer token.
But for the sake of this tutorial, I'm going to remove it.
And simply and simply replace the MCP URL from my workflow.
So I'm going to do it again.
Reduction.
And paste it right here within the quotes.
And save.
Perfect.
Now this is really important.
You need to completely close your desktop app and restart it to make the changes work.
So now when I go to settings again.
Under developer tab, I can see that we successfully connected the application to N810 effectively to
the MCP server because it's running now.
And now when I go to.
Chat.
So in conversation.
And click right here to Search and tools.
Now you have access to our editing workflow.
So to our MCP server.
So let's click on that.
And here we can enable and disable all the tools we just created.
So to update our to dos to list to those.
And actually I forgot to rename one of the nodes.
So I'm going to fix it now.
This one is to create new items.
New tasks.
Create.
Task.
Save.
Right now, let's test it.
So can you please.
Tasks.
All right.
What would you like to use?
An external integration.
So we need to give it permission.
So I'll run.
As you can see now it's using list Todos notes.
So the one on one of the tools here are all your tasks from your to do list.
Incomplete tasks.
Recording a tutorial call.
Dentist.
Completed tasks.
Gym.
Shopping.
I also noticed there are two tasks without name in your list.
Would you like me to help you update those or add names to them?
To recap, the important parts you need to replace are the MCP URL.
From your workflow and your token.
If you configured authentication in your MCP trigger node.
So right here your workflow with the MCP server is active.
The great thing is, once this is set up correctly with the right format for your version, you don't
need to touch the config file again.
So when you add new tools to your MCP server, they will automatically show up in cloud.
Now to demonstrate some workflows.
So how to call separate workflows by your MCP server.
I'm going to go back to our main dashboard.
And creating workflow.
I will call this one.
Content creator.
All right.
First I will click Add First Step and choose when executed by another workflow.
For the input data mode I will keep it on define using fields below, and I will add a new field Right
field.
So the new field I want to add is topic.
I will keep it as a string.
All right.
So the type is string.
And now this creates our test data tool.
So I'm going to clear the default values.
So I'm going to remove this part.
And now add the query property with curly braces.
So.
Like that.
So I'm going to.
And now I will set the value to.
Right.
I think in post.
About.
Automation.
In business.
Great.
I'm going to now I'm going to click save.
So you have a query.
Now let's go back to the canvas.
And this workflow will use an agent to create content for us.
Of course you can use uh you could use a simple LM chain instead.
Both will work but the agent node is more flexible.
So let's add agent instead of basic lm chain.
Now I'll change the source For for prompt to define below and add the topic to the prompt.
So simply grab the query and drop it right here.
I will also include a system message saying.
You are a professional.
Content creator who writes.
Engaging social media posts.
When I grade next I will add chat model.
You can use the chat model of your choice.
I'm going to stick with GPT four.
Great.
Also, if you are running N810 locally, you could use an open source models such as Alama.
So you know it's up to you.
Now let's hit save.
Now let's test this workflow.
All right, let's have a look at the output.
All right, actually, let's improve the prompt to make it more natural.
So I'm going to.
Switch over to expression expanded.
Thanks to this prompt.
The output should be a lot better.
So when the user provides a topic or idea, create a LinkedIn style post that opens with a strong,
relatable, relatable hook or observation.
Breaks down key points using bullet points or a numbered list.
Maintains a clear, concise, and professional tone.
Avoids buzzwords, glitches and emojis, hence that thoughtful insight.
Takeaway or call to observe, to conversation.
Write as if you are sharing your own experience or expertise to engage professionals and encourage discussion.
It is a good prompt.
Now I've prepared this prompt using GPT that's publicly available on GPT store.
It's called Prompt Engineer, so I recommend it if you want to create better prompts for your AI tools
like workflows and agents.
Now I'm going to save this.
And test this workflow again.
Let's wait a second.
And now let's see the output.
In today's rapidly evolving business landscape, the conversation around automation is more relevant
than ever.
Many organizations are recognizing the automation that automation optimization doesn't just streamline
operations, it transforms how you work and engage with our customers.
Here are some key points to consider about the impact of automation in business.
Increase efficiency automation reduces the time spent on repetitive tasks and allowing teams to focus
on higher value activities that drive innovation and growth.
Cost savings and so on.
Yes, I think this post is much better than the previous one.
Alright, great.
Now head back to our, um, MCP server workflow and I will click on tools and select Workflow Tool.
I will name this one.
Uh.
Generate post.
All right.
And for the description.
I will say something like use this tool.
When you need.
To create social media content.
For all I need to do now is select our workflow.
We've just created.
I forgot the name of it.
So let's check.
So it's content creator.
So let's search for content creator.
Yep.
So this is the workflow we just made.
Now since we are using an agent, I can let the AI determine what the topic should be based on user
requests.
And now let's save this workflow.
And now when I restart Cloud Desktop this new tool will appear.
So let's check.
So on the developer tab and then I see running.
Now let's start the chat.
So file new your conversation.
And in turn and as you can see now we can access the tool we just created and add it to our MCP server.
So now I'm going to test it and ask to create a post.
About the future.
For her agents in business applications.
All right.
Great.
So now the now cloud is triggering the workflow.
So allowance.
Now is generating the post.
The future of agents is shaping business operations.
What's changing in 2025.
Process automation intelligent decision making human AI collaboration.
The competitive advantage is clear.
All right.
So it's not perfect.
But you know we want to just to test um if the connection is working now.
All right.
So yeah as you can see this setup is incredibly powerful.
We now have content generation tool available in cloud desktop through MCP server.
And this is just one example of what's possible.
Basically, any workflow you can build in any time becomes an AI tool.
So the possibilities are endless.
When you can connect your AI assistance directly to your business processes and automations.
Great.
Let's move on.
Now I will add three more tools, this time for managing emails.
So we'll create tools to draft email, send emails, and fetch emails.
So let's go to our workflow.
Okay.
Let's move it to the left.
Make some space.
Now let's add another tool.
Search for search for Gmail.
Email tool.
Set automatically the resource message.
Now we want to create draft.
So actually we need to change resource to draft and leave operation as create for the subject message.
Let's set up this to be defined automatically by the AI model.
Since we want the AI to determine what to write based on our request.
Then I will set the image type to HTML so you can have nicely formatted emails.
And for the description I will use.
Use this tool to create.
Image drafts.
All right.
It will help.
I understand.
When to use it.
So now let's add another tool.
First, let's rename this one to indicate what it does.
Create draft.
Now let's add another another tool for fetching emails.
So again search for Gmail.
This time we want to leave a message.
But this operation we want to.
Get money.
For the return all I will set this to false and put a limit Of, let's say, ten.
So it doesn't overwhelm the eye with too many emails.
And I will enable simplify to make the email data easier for the eye to process.
All right.
No filters.
Let's rename it to.
Get emails.
And finally I will add one more tool.
So again Gmail tool.
This time to send emails for us.
So message send again.
Two.
Subject and message fields will all be defined by automatically by the AI model.
So it can determine this based on.
What we ask for.
And I will send and I will set the email type to HTML add option.
And switch append attribution off.
Okay great.
So yes you can use this MCP servers as tools in our other workflows.
And that's really amazing.
So here I have the WhatsApp agent setup from earlier in this lesson to connect our MCP server as a tool.
All I need to do is just click on tools.
Select MCP client tool.
And now I need to Copy endpoint URL.
So let's go back to our MCP server workflow.
Let's switch over to production and copy the URL.
Now let's go back to our agent.
Open MCP client node.
And now let's paste the endpoint here.
Now if I had enabled authentication on the server.
So this is where I would configure those credentials.
What I mean by that is.
Here you can enable authentication by authentication or authentication.
Right here I can specify which tools to include from the server.
So by default I can pull in all the tools, which is what cloud, desktop and cursor do automatically.
But I can include just specific tools.
So click on selected.
And here I can see all the available tools from our server.
So to generate post to create task update to do and list to dos, I will leave it set to all and allow
the agent to decide when to use each tool based on the request.
Now let's save the workflow.
Perfect.
Now make sure both your workflows are active.
Now you can communicate to your agent via WhatsApp and ask it to manage your to do list.
Generate a social media post for you or manage your emails through MCP server.
Now I'm going to show you how to connect the WhatsApp agent we created in the first part of this lesson.
So it will have access to the tools we created through the MCP server.
Now if you want to try and play with it, I will show you how to connect it to your MCP server.
So you will be able to use all the tools.
Also, if you are interested in coding and how to create a simple apps using prompts, I recommend you
check the tutorial I recorded on how to build your own image generator app with loveable and GPT image,
and integrate it with N810 so you can find it.
And in the more advanced project section of this course.
Also, I will prepare more tutorials on web coding and how to use N810 solutions as backend for these
apps.
So now I will show you how to integrate NCP server with this cursor, which is another great use case.
So first of all, download and install cursor from the website.
Go to cursor and when you open cursor.
Go to.
Settings then cursor settings.
Then navigate to Tools and Integrations.
Click on add a custom NCP server.
And paste the same configuration text we used for Cloud Desktop.
And to be more precise, this is not code, it's a JSON configuration snippet.
All right.
And now if you go back to our cursor settings.
Integrations.
And now you can see that N810 is now connected.
And you should have access to all the tools through the MCP server.
Here you can simply enable or disable disable the connection.
So now we can use them by chatting with cursor.
So we just need to open a new chat.
Like that.
So now we have access to our MCP server and all the connected multiple tools.
So we have a content generation to do management emails.
So this gives us some flexibility to manage our workflows directly through conversations with large
language models.
And you know that's actually really cool that whatever you build in any ten becomes available in these
channels.
Now you can use the tools you created by chatting with your agent on WhatsApp or in Cursor or Cloud
Desktop app, whatever option you prefer.
I hope you enjoyed this lesson.
Of course, remember, if you face any issues or something is unclear, please feel free to ask questions
in the Q&A section.
Thank you and see you next time.