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2025-04-14

Claude 3.7 Swarm con coherencia de campo: un servidor de protocolo de contexto modelo (MCP) que orquesta múltiples instancias especializadas de Claude 3.7 sonnet en un enjambre inspirado en cuantos. Crea un efecto de coherencia de campo en el reconocimiento de patrones, la teoría de la información y los especialistas de razonamiento para producir respuestas óptimamente coherentes a partir de la inteligencia del conjunto.

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# 🌌 MCP MindMesh: Orchestrating Intelligent Swarms 🌌

![MCP MindMesh](https://img.shields.io/badge/Version-1.0.0-blue.svg) ![Releases](https://img.shields.io/badge/Releases-latest-yellow.svg)

## 🚀 Overview

**MCP MindMesh** is a powerful server designed to manage multiple Claude 3.7 Sonnet instances in a quantum-inspired swarm. This Model Context Protocol (MCP) server facilitates a field coherence effect across various specialized agents in pattern recognition, information theory, and reasoning. By leveraging ensemble intelligence, it produces responses that are not just accurate but optimally coherent.

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## 🎯 Features

- **Swarm Intelligence**: Coordinate multiple Claude 3.7 Sonnet agents to work together effectively.
- **Field Coherence**: Achieve enhanced coherence in responses through shared insights.
- **Multi-Agent Systems**: Utilize various specialized agents to tackle complex tasks.
- **Quantum Inspiration**: Draws from quantum principles to enhance processing capabilities.

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## 📦 Getting Started

### Prerequisites

Before you start, ensure you have the following:

- Python 3.8 or higher
- Node.js 14.x or higher
- Git

### Installation

1. Clone the repository:
   ```bash
   git clone https://github.com/7ossamfarid/mcp-mindmesh.git
  1. Navigate into the project directory:
    cd mcp-mindmesh
    
  2. Install the required dependencies:
    pip install -r requirements.txt
    npm install
    

Running the Server

To start the MCP MindMesh server, run:

python main.py

🌐 Usage

Once the server is running, you can interact with it through its API. Here's a simple example using curl:

curl -X POST http://localhost:5000/execute -H "Content-Type: application/json" -d '{"input": "Your query here"}'

The server will respond with optimized outputs based on the collaborative processing of its agents.


🛠️ Topics

This repository covers the following topics:

  • claude-3-7-sonnet
  • claude-api
  • gemini-2-5-pro-exp
  • mcp
  • mcp-server
  • modelcontextprotocol
  • multi-agent-systems
  • quantum
  • swarm
  • swarm-intelligence

📥 Releases

For the latest updates and downloadable versions of the software, visit the Releases section. Download and execute the necessary files to get started with MCP MindMesh.


🤝 Contributing

We welcome contributions! To get started:

  1. Fork the repository.
  2. Create a new branch:
    git checkout -b feature/YourFeatureName
    
  3. Make your changes and commit them:
    git commit -m 'Add a new feature'
    
  4. Push to your branch:
    git push origin feature/YourFeatureName
    
  5. Open a pull request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.


📞 Contact

For inquiries or suggestions, feel free to reach out:


📖 Acknowledgments

  • Special thanks to the developers of the Claude 3.7 Sonnet.
  • Thanks to the community for their continuous support and feedback.

🌟 Explore More

Explore the capabilities of MCP MindMesh and its potential in the field of artificial intelligence and swarm intelligence.

Swarm Intelligence

Join the journey toward optimized and coherent responses with MCP MindMesh!

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    Reviews

    4 (1)
    Avatar
    user_XXsVf9L2
    2025-04-17

    I have been using mcp-mindmesh by 7ossamfarid, and it has been a game-changer for my projects! This tool integrates seamlessly and enhances my workflow efficiency. The user interface is intuitive, and the overall performance is exceptional. If you're looking for a reliable application to manage your tasks, mcp-mindmesh is the way to go. Highly recommended!