What is Mcp Hub

MCP Hub is a framework designed for creating and managing servers and clients that utilize the Model Context Protocol (MCP). It utilizes the UV tool to enhance package installation and configuration management processes, making it easier to set up and maintain MCP applications.

Use cases

MCP Hub can be used in scenarios where developers need to build applications that communicate using the Model Context Protocol, such as real-time data sharing between multiple services or integrating machine learning models with user interfaces in a systematic way.

How to use

To use MCP Hub, initialize a new project with ‘uv init’, set up a virtual environment with ‘uv venv’, install the necessary dependencies using ‘uv add’, and create the main server file. To run the server, execute ‘uv run <your_server_file.py>’.

Key features

Key features of MCP Hub include fast package management via the UV tool, easy setup of virtual environments, straightforward installation of required libraries, and the ability to create JupyterHub configurations for enhanced data science workflows.

Where to use

MCP Hub is suitable for use in any context that involves building scalable server-client architectures using the MCP framework. It is particularly useful in data science projects, especially those integrating machine learning models or requiring efficient configuration and dependency management.

MCP Hub Documentation

Overview

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

Why Use UV?

UV simplifies package management and configuration with blazing-fast commands. Learn a few commands to get started, and you’re good to go:

• Initialize a project:

  uv init
  

• Sync Python version and dependencies:

  uv sync
  

For more details, visit the UV GitHub repository.

Motivation

To understand the basics of MCP and get started with creating MCP servers, refer to the MCP Quickstart Server Guide.

Getting Started

How to Create a Sample 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 the Server File

   touch XYZ.py
   

How to Run the MCP Server

To run the server, use the following command:

uv run XYZ.py

Example: Creating a New XYZ Server

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

Recent Updates

Notebooks Directory

The notebooks/ directory has been added to the project. It includes 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

This script downloads the dataset and prints a confirmation message.

AI Folder

The ai/ folder contains various subdirectories and scripts related to computer vision and artificial intelligence. Below is an overview of its structure and contents:

Subdirectories and Files

01_image_handling

• basic_manipulations.py: Basic image manipulation techniques.
• blue_image.png: Sample image for testing.
• hello_cv.py: A simple script to demonstrate computer vision basics.
• image_representation.py: Explains image representation in computer vision.
• read_display_save.py: Script to read, display, and save images.
• README.md: Documentation for this subdirectory.

02_image_preprocessing

• augmentation.py: Image augmentation techniques.
• normalization.py: Image normalization methods.

03_feature_extraction

• hog_extraction.py: Extracts Histogram of Oriented Gradients (HOG) features.
• sift_surf_extraction.py: Demonstrates SIFT and SURF feature extraction.

04_basic_ml_concepts

• hog_svm_classifier.py: Implements a classifier using HOG features and SVM.

05_deep_learning_cnn

• cnn_architecture.py: Defines a Convolutional Neural Network (CNN) architecture.

06_image_classification

• train_classifier.py: Script to train an image classifier.

07_object_detection

• basic_object_detection.py: Demonstrates basic object detection techniques.

08_image_segmentation

• basic_segmentation.py: Explains basic image segmentation methods.

09_datasets

• download_cifar10.py: Script to download the CIFAR-10 dataset.

10_utils

• image_utils.py: Utility functions for image processing.

Additional Files

• main.py: Entry point for AI-related scripts.
• pyproject.toml: Configuration file for the project.
• README.md: Documentation for the ai/ folder.
• run.sh: Shell script to execute AI-related tasks.
• uv.lock: Lock file for dependencies.