https://github.com/reddy-sh/mcp-hub
MCP Hub is a comprehensive framework for building, managing, and deploying Model Context Protocol (MCP) clients and servers. It provides tools and configurations to enable seamless integration and execution of end-to-end MCP workflows.
https://github.com/reddy-sh/mcp-hub
llm llmapps mcp mcp-client mcp-server modelcontextprotocol python
Last synced: 4 months ago
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MCP Hub is a comprehensive framework for building, managing, and deploying Model Context Protocol (MCP) clients and servers. It provides tools and configurations to enable seamless integration and execution of end-to-end MCP workflows.
- Host: GitHub
- URL: https://github.com/reddy-sh/mcp-hub
- Owner: reddy-sh
- License: apache-2.0
- Created: 2025-04-06T22:59:05.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-10T04:03:50.000Z (about 1 year ago)
- Last Synced: 2025-04-13T11:14:43.878Z (about 1 year ago)
- Topics: llm, llmapps, mcp, mcp-client, mcp-server, modelcontextprotocol, python
- Language: Python
- Homepage: https://reddy.sh
- Size: 335 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-mcp-servers - **mcp-hub** - MCP Hub is a comprehensive framework for building, managing, and deploying Model Context Protocol (MCP) clients and servers. It provides tools and configurations to enable seamless integration and execution of end-to-end MCP workflows. `python` `llm` `llmapps` `mcp` `mcp-client` `pip install git+https://github.com/reddy-sh/mcp-hub` (🤖 AI/ML)
README
# 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:
```bash
uv init
```
- Sync Python version and dependencies:
```bash
uv sync
```
For more details, visit the [UV GitHub repository](https://github.com/astral-sh/uv).
## Motivation
To understand the basics of MCP and get started with creating MCP servers, refer to the [MCP Quickstart Server Guide](https://modelcontextprotocol.io/quickstart/server).
## Getting Started
### How to Create a Sample MCP Server
1. **Create a New Project Directory**
```bash
uv init XYZ
cd XYZ
```
2. **Set Up a Virtual Environment**
```bash
uv venv
source .venv/bin/activate
```
3. **Install Dependencies**
```bash
uv add "mcp[cli]" httpx
```
4. **Create the Server File**
```bash
touch XYZ.py
```
### How to Run the MCP Server
To run the server, use the following command:
```bash
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:
```bash
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.