MCP Hub - MCP Server | MCP Directory

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

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 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.