MindMesh MCP Server

A Model Context Protocol (MCP) server implementation that creates a quantum-inspired swarm of Claude 3.7 Sonnet instances with field coherence optimization. This server enables enriched reasoning through multiple specialized LLM instances that work together with emergent properties.

Features

• Quantum-Inspired Field Computing: Uses a field-based model to maintain coherence between Claude instances
• WebContainer Integration: Full stack sandboxed environment for execution
• PGLite with Vector Storage: Efficient vector database with pgvector extension
• Multiple Claude Specializations: Instances focus on pattern recognition, information synthesis, and reasoning
• Coherence Optimization: Selects the most coherent outputs across instances
• Extended Thinking Support: Optional 128k token thinking capability
• Live Query Updates: Real-time coherence notifications through PGLite live extension
• VoyageAI Embeddings: High-quality embeddings using VoyageAI's state-of-the-art models (voyage-3-large)

Prerequisites

• Node.js 18.x or higher
• Anthropic API key with access to Claude 3.7 Sonnet
• VoyageAI API key (optional but recommended for better embeddings)

Installation

1. Clone this repository:

   git clone https://github.com/wheattoast11/mcp-mindmesh.git
   cd mcp-mindmesh
   

2. Install dependencies:

   npm install
   

3. Create a .env file by copying the template:

   cp .env.template .env
   

4. Edit .env and add your Anthropic API key, VoyageAI API key (optional), and adjust other settings as needed.

Usage

Starting the Server

Build and start the server:

npm run build
npm start

For development with auto-reload:

npm run dev

Connecting to the Server

You can connect to this MCP server using any MCP client, such as:

1. Claude Desktop Application for Windows (official Anthropic client)
2. Cursor IDE's agent capabilities
3. Cline VSCode extension
4. Any other MCP-compatible client

The server will be available at http://localhost:3000 by default (or whichever port you specified in the .env file).

Using the Reasoning Tool

The main tool provided by this server is reason_with_swarm. This tool takes a prompt and processes it through multiple specialized Claude instances, returning the most coherent result.

Example usage in Claude Desktop:

Please use the swarm to analyze the relationship between quantum field theory and consciousness.

Configuration Options

All configuration options can be set in the .env file:

Architecture

The server architecture consists of:

1. MCP Server Layer: Implements the Model Context Protocol (2025-03-26 specification)
2. WebContainer Layer: Provides sandboxed environment for execution
3. PGLite Vector Database: Stores state vectors with pgvector extension
4. Claude Swarm Layer: Manages multiple specialized Claude instances
5. Quantum Field Layer: Handles field coherence and optimization
6. Embedding Layer: Generates high-quality embeddings using VoyageAI models

Requests flow through these layers as follows:

Client Request → MCP Server → Swarm Processing → Claude API → Coherence Optimization → Response

Advanced Features

Web Container Integration

The server uses WebContainer technology for a fully sandboxed environment, providing:

• Isolated execution environment
• Full stack capabilities
• File system access
• Network communication

PGLite with Vector Extension

PGLite provides:

• Client-side PostgreSQL database compiled to WebAssembly
• Vector operations through pgvector extension
• Live query notifications for real-time updates
• Persistent storage across sessions

Field Coherence Optimization

The coherence optimization system:

1. Processes a query through multiple specialized Claude instances
2. Generates state vectors for each response
3. Calculates coherence metrics between instances
4. Selects the most coherent output
5. Maintains a dynamic field state in the vector database

VoyageAI Embeddings

The server uses VoyageAI's state-of-the-art embedding models for:

• High-quality state vector generation
• More accurate coherence calculations
• Better field modeling and optimization

When VoyageAI API key is not available, the server falls back to a simpler, deterministic embedding method.

Development

Project Structure

• src/index.ts: Main entry point
• src/server.ts: Core server implementation
• .env: Configuration file
• package.json: Dependencies and scripts

Building

npm run build

This will compile TypeScript to JavaScript in the dist directory.

Testing

npm test

License

MIT

Acknowledgements

This project uses the following technologies:

• Model Context Protocol (2025-03-26 spec)
• Anthropic Claude API
• VoyageAI Embeddings
• WebContainer API
• PGLite
• ElectricSQL vector extension