Neo Mcps
What is Neo Mcps
neo-mcps is a collection of on-demand Model Context Protocol (MCP) servers designed to enhance AI interactions by providing specific functionalities that can be integrated with MCP-compatible AI systems.
Use cases
Use cases include providing audio notifications for AI responses in chatbots, enhancing user experience in virtual assistants, and integrating sound feedback in educational AI applications.
How to use
To use neo-mcps, clone the repository, navigate to the desired server directory, and install the necessary dependencies using npm or yarn. Ensure you have Node.js (v16 or higher) and an MCP-compatible AI system.
Key features
Key features include customizable sound notifications that provide audio feedback when an LLM finishes its reply, seamless integration with MCP-compatible AI systems, and a lightweight design for ease of use.
Where to use
neo-mcps can be used in various fields where AI interactions are prevalent, such as customer service, virtual assistants, and any application requiring enhanced user feedback.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Neo Mcps
neo-mcps is a collection of on-demand Model Context Protocol (MCP) servers designed to enhance AI interactions by providing specific functionalities that can be integrated with MCP-compatible AI systems.
Use cases
Use cases include providing audio notifications for AI responses in chatbots, enhancing user experience in virtual assistants, and integrating sound feedback in educational AI applications.
How to use
To use neo-mcps, clone the repository, navigate to the desired server directory, and install the necessary dependencies using npm or yarn. Ensure you have Node.js (v16 or higher) and an MCP-compatible AI system.
Key features
Key features include customizable sound notifications that provide audio feedback when an LLM finishes its reply, seamless integration with MCP-compatible AI systems, and a lightweight design for ease of use.
Where to use
neo-mcps can be used in various fields where AI interactions are prevalent, such as customer service, virtual assistants, and any application requiring enhanced user feedback.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
Neo-MCPs
A collection of on-demand Model Context Protocol (MCP) servers for enhancing AI interactions.
🚀 Overview
This repository contains custom MCP servers that can be used to extend the capabilities of AI assistants. Each server provides specific functionality that can be integrated with MCP-compatible AI systems.
🔌 Servers
🔊 Sound Notification
A simple MCP server that plays a notification sound when an LLM finishes its reply. This helps provide audio feedback when interacting with AI assistants.
Features:
- 🎵 Plays configurable system sounds
- 🔄 Integrates seamlessly with MCP-compatible AI systems
- 🪶 Lightweight and easy to use
# Quick install
cd servers/sound-notification && npm install
🏁 Getting Started
Prerequisites
- 📦 Node.js (v16 or higher)
- 📦 npm or yarn
- 🤖 An MCP-compatible AI system
Installation
-
Clone this repository:
git clone https://github.com/yourusername/neo-mcps.git cd neo-mcps -
Install dependencies for a specific server:
cd servers/sound-notification npm install -
Build the server:
npm run build -
Use the server with your MCP-compatible AI system.
💻 Development
Each server is contained in its own directory under the servers/ folder. To create a new server:
- Create a new directory under
servers/ - Initialize a new Node.js project
- Implement the MCP server functionality
- Add appropriate documentation
🙏 Acknowledgments
- Model Context Protocol for providing the framework that makes these servers possible
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
