MCP Hub Documentation

MCP Hub is a framework for creating and managing Model Context Protocol (MCP) servers and clients, integrating the UV tool to simplify package management and configuration. The project includes AI - related computer vision scripts and dataset processing tools, supporting rapid deployment and development.

Developer tools Artificial intelligence chatbots #MCP framework #UV tool #AI scripts #Computer vision .Python

rating : 2.5 points

downloads : 23

update time : 2025-04-23

What is an MCP server?

The MCP server is a high - performance server framework based on the Model Context Protocol (MCP), used to handle model context communication requirements. It supports the rapid creation and management of servers and provides stable and reliable communication interfaces.

How to use an MCP server?

Through simple initialization and configuration, you can quickly set up an MCP server. Just a few commands are needed to complete the installation, running, and service deployment.

Applicable scenarios

The MCP server is suitable for scenarios that require high - efficiency model communication, such as AI model inference, real - time data analysis, and distributed system collaboration.

Main features

Quick initializationQuickly create a project and set up the environment through the UV tool.

Efficient communication protocolAchieve low - latency and high - throughput data transmission based on the MCP protocol.

Modular expansionSupport plug - in expansion for easy addition of new functions.

Advantages and limitations

Advantages

Quick setup and deployment

Stable communication performance

Easy to expand and maintain

Limitations

Some dependence on the network environment

Additional learning costs may be required for advanced functions

How to use

Create a project

Use the UV tool to initialize a new MCP server project.

Set up a virtual environment

Activate the virtual environment to ensure dependency isolation.

Install dependencies

Install necessary dependency packages to support MCP functions.

Write a server script

Create and edit the main server file (e.g., my_mcp_server.py).

Start the server

Run the script to start the MCP server.

Usage examples

Create and run an MCP serverDemonstrate how to create and run an MCP server instance from scratch.

Download the CIFAR - 10 datasetShow how to use the MCP server to download common datasets.

Frequently Asked Questions

How to solve dependency conflicts when starting the server?

Which operating systems does the MCP server support?

Related resources

MCP official documentation

Gain in - depth understanding of the MCP protocol and its application scenarios.

UV tool GitHub repository

Get the source code and support for the UV tool.

🚀 MCP Console Documentation

The MCP Console is a framework for creating and managing Model Context Protocol (MCP) servers and clients. It leverages the uv tool for rapid package installation and configuration management.

🚀 Quick Start

Why Use UV?

UV simplifies package management and configuration, and its command execution is extremely fast. You only need to learn a few commands to get started:

Initialize a project:
```
uv init
```
Synchronize Python version and dependencies:
```
uv sync
```

For more details, visit the UV GitHub repository.

Motivation

To understand the basics of MCP and start creating an MCP server, refer to the MCP Quickstart Server Guide.

How to Create an Example MCP Server

Create a new project directory
```
uv init XYZ
cd XYZ
```
Set up a virtual environment
```
uv venv
source .venv/bin/activate
```
Install dependencies
```
uv add "mcp[cli]" httpx
```
Create a server file
```
touch XYZ.py
```

How to Run an MCP Server

To run the server, use the following command:

uv run XYZ.py

Example: Create a New XYZ Server

Follow the above steps to create and run a new XYZ server. Replace XYZ with your desired project name.

📦 Installation

Initialization

uv init project_name

Starting the Development Environment

uv run -d

Creating a New Feature Module

uv add feature_module

💻 Usage Examples

Basic Usage

To create and run an MCP server, follow these steps:

Create a new project directory:
```
uv init XYZ
cd XYZ
```
Set up a virtual environment:
```
uv venv
source .venv/bin/activate
```
Install dependencies:
```
uv add "mcp[cli]" httpx
```
Create a server file:
```
touch XYZ.py
```
Run the server:

uv run XYZ.py

📚 Documentation

Recent Updates

Notebook Directory

The notebooks/ directory has been added to the project, containing configuration files and scripts for setting up and running JupyterHub. Key files include:

jupyterhub_config.py: Configuration for JupyterHub.
start_jupyterhub.sh: Script to start the JupyterHub server.

CIFAR - 10 Dataset Downloader

A new script has been added under ai/computer-vision/09_datasets/ to download the CIFAR - 10 dataset using TensorFlow/Keras. To use it, run:

python ai/computer-vision/09_datasets/download_cifar10.py

The script downloads the dataset and prints a confirmation message.

AI Folder

The ai/ folder contains various sub - folders and AI - related scripts. Here is an overview of its structure and contents:

01_image_handling

basic_manipulations.py: Basic image processing techniques.
blue_image.png: An example image for testing.
hello_cv.py: A simple script demonstrating computer vision basics.
image_representation.py: Explains how images are represented in computer vision.
read_display_save.py: A script to read, display, and save images.
README.md: Documentation for this sub - folder.