Memory Context Manager_v2

by Big0290

197 downloads

Not rated

GitHub

About

🧠 Deep Project Analysis: Memory Context Manager v2

Details

Author: Big0290
Downloads: 197
Categories: Knowledge Base, AI

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Memory Context Manager_v2

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

From the repository

To use Memory Context Manager_v2, run its MCP server instance and then access its web UI by cloning the UI repository, installing dependencies with yarn install, starting the development server with yarn rw dev, and navigating to http://localhost:8910. The UI provides real-time monitoring, evolution tracking, and system control.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "memory context manager_v2": {
            "memory-context-manager-v2": {
                "command": "docker",
                "args": [
                    "run",
                    "-i",
                    "--rm",
                    "memory-context-manager-v2:latest",
                    "python",
                    "main.py"
                ],
                "env": {
                    "MCP_LOG_LEVEL": "INFO",
                    "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
                }
            }
        }
    }
}

McpServers

{
    "memory-context-manager-v2": {
        "command": "docker",
        "args": [
            "run",
            "-i",
            "--rm",
            "memory-context-manager-v2:latest",
            "python",
            "main.py"
        ],
        "env": {
            "MCP_LOG_LEVEL": "INFO",
            "OLLAMA_BASE_URL": "http://host.docker.internal:11434"
        }
    }
}

🚀 MCP Server UI - Memory Context Manager v2

A beautiful, modern web interface for the Memory Context Manager v2 MCP server, built with RedwoodJS and Chakra UI. This UI showcases our Continuous Self-Evolution System and provides real-time monitoring of our autonomous AI capabilities.

✨ Features

🎯 Dashboard Overview

- Real-time System Health monitoring with beautiful progress bars
- Evolution Engine Status showing continuous self-improvement
- MCP Server Metrics with connection and tool availability
- Recent Activity Feed tracking all system operations
- Quick Actions for immediate system control

🧠 Evolution Engine Interface

- Comprehensive Evolution Metrics with detailed breakdowns
- Scheduled Tasks Management with priority and timing
- Recent Evolutions History showing learning progress
- Learning Sources Tracking from documentation ingestion
- System Health Monitoring with background processes

🎨 Modern UI/UX

- Chakra UI Components for consistent, accessible design
- Responsive Layout that works on all devices
- Custom Theme with brand colors and evolution accents
- Interactive Elements with hover effects and animations
- Professional Dashboard layout with sidebar navigation

🏗️ Architecture

Frontend Stack

- RedwoodJS - Full-stack React framework
- Chakra UI - Modern component library
- TypeScript - Type-safe development
- React Icons - Beautiful iconography

Backend Integration

- MCP Server - Memory Context Manager v2
- Evolution Engine - Continuous self-improvement
- Real-time Data - Live system monitoring
- API Endpoints - RESTful communication

🚀 Getting Started

Prerequisites

- Node.js 18+
- Yarn package manager
- MCP Server running (Memory Context Manager v2)

Installation

1. Clone the repository

   git clone <repository-url>
   cd mcp-server-ui

2. Install dependencies

   yarn install

3. Start the development server

   yarn rw dev

4. Open your browser
Navigate to http://localhost:8910

📱 Pages & Features

🏠 Dashboard Page (`/`)

- System Health Overview: Real-time metrics with progress bars
- Evolution Engine Status: Current evolution progress and statistics
- MCP Server Status: Connection status and tool availability
- Recent Activity: Timeline of system operations
- Quick Actions: Immediate system control buttons

⚡ Evolution Engine Page (`/evolution-engine`)

- Evolution Metrics: Detailed breakdown of system improvements
- Scheduled Tasks: Management of upcoming evolution tasks
- Recent Evolutions: History of completed improvements
- Learning Sources: Documentation sources and learning progress
- System Health: Background processes and resource usage

🎨 Design System

Color Palette

- Brand Colors: Professional blue tones for primary elements
- Evolution Colors: Purple accents for evolution-related features
- Status Colors: Green (success), Yellow (warning), Red (error)
- Neutral Colors: Gray scale for text and borders

Component Library

- Cards: Clean, elevated containers for content
- Progress Bars: Visual representation of metrics
- Badges: Status indicators and labels
- Tables: Organized data presentation
- Tabs: Organized content sections
- Alerts: Important information notifications

🔧 Configuration

Environment Variables

# MCP Server Configuration
MCP_SERVER_URL=http://localhost:8000
MCP_SERVER_TIMEOUT=30000
Evolution Engine Settings
EVOLUTION_CHECK_INTERVAL=5000
EVOLUTION_MAX_CONCURRENT=3

Theme Customization

The Chakra UI theme can be customized in web/src/App.tsx:

const theme = extendTheme({
  colors: {
    brand: {
      / Custom brand colors /
    },
    evolution: {
      / Custom evolution colors /
    },
  },
  components: {
    Button: {
      / Custom button styles /
    },
  },
})

📊 Data Integration

MCP Server Communication

- Real-time Updates: Live data from evolution engine
- Status Monitoring: Server health and connection status
- Tool Management: Available MCP tools and their status
- Performance Metrics: System performance indicators

Evolution Engine Data

- Learning Progress: Documentation ingestion status
- Evolution Metrics: Performance improvements over time
- Task Scheduling: Upcoming evolution tasks
- System Health: Resource usage and background processes

🚀 Deployment

Production Build

# Build the application
yarn rw build
Start production server
yarn rw serve

Docker Deployment

# Build Docker image
docker build -t mcp-server-ui .
Run container
docker run -p 8910:8910 mcp-server-ui

🔍 Monitoring & Analytics

System Metrics

- Overall Health Score: Composite system performance
- Performance Metrics: Speed and efficiency indicators
- Intelligence Score: AI capability measurements
- Adaptability Rating: System flexibility and learning

Evolution Tracking

- Success Rate: Percentage of successful evolutions
- Learning Sources: Documentation sources and impact
- Improvement Trends: Performance over time
- Resource Usage: Memory and CPU consumption

🧪 Testing

Run Tests

# Unit tests
yarn rw test
Integration tests
yarn rw test --watch
E2E tests
yarn rw test:e2e

Test Coverage

# Generate coverage report
yarn rw test --coverage

🤝 Contributing

Development Workflow

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

Code Standards

- TypeScript: Strict type checking enabled
- ESLint: Code quality and consistency
- Prettier: Code formatting
- Chakra UI: Component library usage

📚 Documentation

API Reference

- MCP Server Tools: Available MCP server functions
- Evolution Engine: Continuous self-improvement API
- System Health: Monitoring and metrics endpoints

Component Library

- Chakra UI Components: Usage examples and props
- Custom Components: Dashboard-specific components
- Layout System: Responsive design patterns

🌟 Showcase

This UI demonstrates the power of our Continuous Self-Evolution System:

- 🎯 Autonomous Learning: System learns from documentation automatically
- 📈 Performance Improvement: Real-time metrics showing system evolution
- 🔄 Background Processing: Continuous optimization without user intervention
- 🧠 Intelligent Adaptation: System adapts to new knowledge sources
- 📊 Visual Monitoring: Beautiful dashboards for system oversight

🔮 Future Enhancements

Planned Features

- Real-time WebSocket updates for live data
- Advanced Analytics with charts and graphs
- User Management with role-based access
- API Documentation with interactive testing
- Mobile App for on-the-go monitoring

Integration Possibilities

- Grafana Dashboards for advanced metrics
- Slack Notifications for system alerts
- GitHub Integration for code-based learning
- CI/CD Pipeline for automated deployment

📄 License

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

🙏 Acknowledgments

- RedwoodJS Team for the amazing full-stack framework
- Chakra UI Team for the beautiful component library
- MCP Community for the Model Context Protocol
- Open Source Contributors for inspiration and tools

---

Built with ❤️ using RedwoodJS + Chakra UI

Showcasing the future of autonomous AI systems 🚀🧠✨

No reviews yet — be the first

Use Google, GitHub, or an email account so ratings stay tied to real people.

Email sign in

No reviews posted yet.

About

Details

Jump to

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

🚀 MCP Server UI - Memory Context Manager v2

✨ Features

🎯 Dashboard Overview

🧠 Evolution Engine Interface

🎨 Modern UI/UX

🏗️ Architecture

Frontend Stack

Backend Integration

🚀 Getting Started

Prerequisites

Installation

📱 Pages & Features

🏠 Dashboard Page (/)

⚡ Evolution Engine Page (/evolution-engine)

🎨 Design System

Color Palette

Component Library

🔧 Configuration

Environment Variables

Evolution Engine Settings

Theme Customization

📊 Data Integration

MCP Server Communication

Evolution Engine Data

🚀 Deployment

Production Build

Start production server

Docker Deployment

Run container

🔍 Monitoring & Analytics

System Metrics

Evolution Tracking

🧪 Testing

Run Tests

Integration tests

E2E tests

Test Coverage

🤝 Contributing

Development Workflow

Code Standards

📚 Documentation

API Reference

Component Library

🌟 Showcase

🔮 Future Enhancements

Planned Features

Integration Possibilities

📄 License

🙏 Acknowledgments

Install

More like this

Aws Kb Retrieval Server

Memory

Context 7

1Panel

🔎 GPT Researcher

Serena

🏠 Dashboard Page (`/`)

⚡ Evolution Engine Page (`/evolution-engine`)