In this tutorial, we will delve into the exciting world of creating a custom MCP (Model Contest Protocol) server using Python and connecting it to large language models. Even if you’re just starting out, by the end of this guide, you’ll have your own functional MCP server up and running, ready to explore the possibilities of AI. We’ll use tools like UV (a package manager), Cloud Desktop, and Fast MCP library to simplify the process.
Let’s get started on this journey into the future of AI!
## Step-by-Step Guide
### Step 1: Set Up Necessary Tools
Before you dive into creating your server, make sure you have the following tools installed:
– **UV Package Manager:** This is a powerful tool similar to PIP but more suited for our needs today.
– **Cloud Desktop:** A platform developed by Antropic for testing MCP servers.
– **Python SDK (Fast MCP):** This library will help you interact with the MCP servers.
#### Installation Instructions
– **For macOS and Linux:**
Open your terminal and copy this command to install UV:
“`bash
curl -sSL https://your-link-to-uv-install.sh | bash
“`
– **For Windows:**
Use PowerShell to install UV by referring to the installation guide on the official site.
Make sure to check that UV was installed correctly by running:
“`bash
uv –version
“`
### Step 2: Prepare Your Development Environment
Create a new directory for your project and initialize a new UV project. Here’s how:
1. Open your terminal and navigate to the directory you want to work in.
2. Type the following command to initialize a new UV project:
“`bash
uv init .
“`
3. This will create a new project with the necessary files.
### Step 3: Install Required Dependencies
Modify your project dependencies by installing the Fast MCP library. You can do this using:
“`bash
uv install FastMCP
“`
Make sure your Python version is up-to-date (at least version 3.10); otherwise, unexpected issues may arise.
### Step 4: Create Your MCP Server Script
Now that your environment is set up, it’s time to create the code for your MCP server.
1. Create a new file named `main.py` in your project directory.
2. Open this file and start writing the necessary logic to create the server:
“`python
from fast_mcp import MCP
# Create your server instance
mcp = MCP(name=”DemoServer”)
# Define your tools and resources here
“`
### Step 5: Connect Your MCP Server to Cloud Desktop
To run your server on Cloud, you need to execute:
“`bash
uv run mcp install main.py
“`
If any errors appear when connecting to Cloud, ensure your configuration settings are correct. You will likely need to specify the absolute path for UV commands.
### Step 6: Debugging Common Connection Issues
If you encounter connectivity issues, you may need to adjust the configuration file:
1. Open the Cloud settings.
2. Find the “Developer” section and edit your configuration file to specify the absolute path to UV. Use the command:
“`bash
which uv
“`
This will provide you the necessary path.
### Step 7: Create Core Functions
You can now create your server functionality, including tools to add thoughts and read from your journal. Here’s a sample structure for these functions:
– **Tool to add thoughts:**
“`python
@mcp.tool
def add_thought(thought: str) -> str:
with open(“journal.txt”, “a”) as f:
f.write(thought + “\n”)
return “Thought added.”
“`
– **Tool to read latest thought:**
“`python
@mcp.tool
def read_latest() -> str:
with open(“journal.txt”, “r”) as f:
lines = f.readlines()
return lines[-1] if lines else “No thoughts yet.”
“`
### Step 8: Testing Your MCP Server
After setting up your server and tools, run the Cloud Desktop application. Use the MCP server commands to add new thoughts and read them back, testing the functionality you built.
### Step 9: Engage with Your Server
You can now interact with your MCP server through Cloud, adding thoughts such as:
“`text
Today was a good day.
I like coding, especially Python.
“`
You can query its response and see how it interacts based on the inputs provided.
### Step 10: Next Steps and Future Enhancements
Explore ways to enhance your MCP server with more tools, such as summarizing thoughts, performing analysis based on inputs, or integrating more advanced capabilities with different AI models.
## Wrap-up & Next Steps
Congratulations! You’ve successfully created a custom MCP server in Python and connected it to large language models. This opens up numerous possibilities for building more sophisticated interactions.
If you enjoyed this guide, consider diving deeper into MCP servers with advanced topics or engaging with our community for further enhancement ideas.
