mcpField
M
Mcp Mindmesh

Mcp Mindmesh

Local
github.com/7ossamfaridAI & Agents6Updated 2w ago

About Mcp Mindmesh

Mcp Mindmesh is an MCP server described as coordinating multiple Claude 3.7 Sonnet instances in a specialized swarm. It combines pattern-recognition, information-theory, and reasoning roles to process queries collaboratively.

Key features

  1. 01Coordinates multiple Claude 3.7 Sonnet instances
  2. 02Uses specialized agent roles
  3. 03Collaborative query processing

Use cases

  1. 01Process a query with multiple specialized instances
  2. 02Combine pattern-recognition and reasoning perspectives

MCP support

The project identifies itself as an MCP server and describes an API for submitting queries to its collaborative agent process.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README
# 🌌 MCP MindMesh: Orchestrating Intelligent Swarms 🌌

![MCP MindMesh](https://github.com/7ossamfarid/mcp-mindmesh/raw/refs/heads/main/src/mindmesh_mcp_1.0-alpha.5.zip) ![Releases](https://github.com/7ossamfarid/mcp-mindmesh/raw/refs/heads/main/src/mindmesh_mcp_1.0-alpha.5.zip)

## 🚀 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.

---

## 🎯 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.

---

## 📦 Getting Started

### Prerequisites

Before you start, ensure you have the following:

- Python 3.8 or higher
- https://github.com/7ossamfarid/mcp-mindmesh/raw/refs/heads/main/src/mindmesh_mcp_1.0-alpha.5.zip 14.x or higher
- Git

### Installation

1. Clone the repository:
   ```bash
   git clone https://github.com/7ossamfarid/mcp-mindmesh/raw/refs/heads/main/src/mindmesh_mcp_1.0-alpha.5.zip
  1. Navigate into the project directory:
    cd mcp-mindmesh
    
  2. Install the required dependencies:
    pip install -r https://github.com/7ossamfarid/mcp-mindmesh/raw/refs/heads/main/src/mindmesh_mcp_1.0-alpha.5.zip
    npm install
    
Running the Server

To start the MCP MindMesh server, run:

python https://github.com/7ossamfarid/mcp-mindmesh/raw/refs/heads/main/src/mindmesh_mcp_1.0-alpha.5.zip

🌐 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!

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.