MCP Hub Documentation

🚀 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

1. Create a new project directory

   uv init XYZ
   cd XYZ
   

2. Set up a virtual environment

   uv venv
   source .venv/bin/activate
   

3. Install dependencies

   uv add "mcp[cli]" httpx
   

4. 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:

1. Create a new project directory:

   uv init XYZ
   cd XYZ
   

2. Set up a virtual environment:

   uv venv
   source .venv/bin/activate
   

3. Install dependencies:

   uv add "mcp[cli]" httpx
   

4. Create a server file:

   touch XYZ.py
   

5. 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.

02_feature_detection

• corner_detection.py: An example of corner detection.
• edge_detection.py: An example of edge detection.
• feature_detection.py: An example of feature detection.
• README.md: Documentation for this sub - folder.

03_image_segmentation

• image_segmentation.py: An example of image segmentation.
• segmentation_methods.py: Implementations of different segmentation methods.
• README.md: Documentation for this sub - folder.

04_object_detection

• object_detection.py: An example of object detection.
• detection_models.py: Implementations of object detection models.
• README.md: Documentation for this sub - folder.

05_face_recognition

• face_detection.py: An example of face detection.
• face_recognition.py: An example of face recognition.
• face_models.py: Implementations of face recognition models.
• README.md: Documentation for this sub - folder.