Kiro MCP Memory

Enhanced MCP Memory

⚡ Optimized for Claude Sonnet 4 - This MCP server works best with Claude Sonnet 4 for optimal performance and AI-powered features.

An enhanced MCP (Model Context Protocol) server for intelligent memory and task management, designed for AI assistants and development workflows. Features semantic search, automatic task extraction, knowledge graphs, and comprehensive project management.

✨ Key Features

🧠 Intelligent Memory Management

Semantic search using sentence-transformers for natural language queries
Automatic memory classification with importance scoring
Duplicate detection and content deduplication
File path associations for code-memory relationships
Knowledge graph relationships with automatic similarity detection

📋 Advanced Task Management

Auto-task extraction from conversations and code comments
Priority and category management with validation
Status tracking (pending, in_progress, completed, cancelled)
Task-memory relationships in knowledge graph
Project-based organization

🔧 Enterprise Features

Performance monitoring with detailed metrics
Health checks and system diagnostics
Automatic cleanup of old data and duplicates
Database optimization tools
Comprehensive logging and error tracking

🚀 Easy Deployment

uvx compatible for one-command installation
Zero-configuration startup with sensible defaults
Environment variable configuration
Cross-platform support (Windows, macOS, Linux)

🏗️ Project Structure

enhanced-mcp-memory/
├── mcp_server_enhanced.py    # Main MCP server
├── memory_manager.py         # Core memory/task logic
├── database.py              # Database operations
├── requirements.txt         # Python dependencies
├── setup.py                # Package configuration
├── data/                   # SQLite database storage
├── logs/                   # Application logs
└── tests/                  # Test files

🚀 Quick Start

Option 1: Using uvx (Recommended)

# Install and run with uvx
uvx enhanced-mcp-memory

Option 2: Manual Installation

# Clone and install
git clone https://github.com/cbunting99/enhanced-mcp-memory.git
cd enhanced-mcp-memory
pip install -e .

# Run the server
enhanced-mcp-memory

Option 3: Development Setup

# Clone repository
git clone https://github.com/cbunting99/enhanced-mcp-memory.git
cd enhanced-mcp-memory

# Install dependencies
pip install -r requirements.txt

# Run directly
python mcp_server_enhanced.py

⚙️ MCP Configuration

Add to your MCP client configuration:

For uvx installation:

{
  "mcpServers": {
    "memory-manager": {
      "command": "uvx",
      "args": ["enhanced-mcp-memory"],
      "env": {
        "LOG_LEVEL": "INFO",
        "MAX_MEMORY_ITEMS": "1000",
        "ENABLE_AUTO_CLEANUP": "true"
      }
    }
  }
}

For local installation:

{
  "mcpServers": {
    "memory-manager": {
      "command": "python",
      "args": ["mcp_server_enhanced.py"],
      "cwd": "/path/to/enhanced-mcp-memory",
      "env": {
        "LOG_LEVEL": "INFO",
        "MAX_MEMORY_ITEMS": "1000",
        "ENABLE_AUTO_CLEANUP": "true"
      }
    }
  }
}

🛠️ Available Tools

Core Memory Tools

get_memory_context(query) - Get relevant memories and context
create_task(title, description, priority, category) - Create new tasks
get_tasks(status, limit) - Retrieve tasks with filtering
get_project_summary() - Get comprehensive project overview

System Management Tools

health_check() - Check server health and connectivity
get_performance_stats() - Get detailed performance metrics
cleanup_old_data(days_old) - Clean up old memories and tasks
optimize_memories() - Remove duplicates and optimize storage
get_database_stats() - Get comprehensive database statistics

🔧 Configuration Options

Configure via environment variables:

Variable	Default	Description
`LOG_LEVEL`	`INFO`	Logging level (DEBUG, INFO, WARNING, ERROR)
`MAX_MEMORY_ITEMS`	`1000`	Maximum memories per project
`CLEANUP_INTERVAL_HOURS`	`24`	Auto-cleanup interval
`ENABLE_AUTO_CLEANUP`	`true`	Enable automatic cleanup
`MAX_CONCURRENT_REQUESTS`	`5`	Max concurrent requests
`REQUEST_TIMEOUT`	`30`	Request timeout in seconds

🧪 Testing

Run the test suite to verify functionality:

# Run all tests
python test_enhanced_features.py
python test_new_project_system.py
python test_project_tools.py

# Test MCP protocol
python test_mcp_protocol.py

📊 Performance & Monitoring

The server includes built-in performance tracking:

Response time monitoring for all tools
Success rate tracking with error counts
Memory usage statistics
Database performance metrics
Automatic health checks

Access via the get_performance_stats() and health_check() tools.

🗄️ Database

SQLite for reliable, file-based storage
Automatic schema migrations for updates
Comprehensive indexing for fast queries
Built-in backup and optimization tools
Cross-platform compatibility

Default location: ./data/mcp_memory.db

🔍 Semantic Search

Natural language queries - "Find memories about database optimization"
Similarity-based matching using embeddings
Configurable similarity thresholds
Automatic model downloading (~90MB on first run)

📝 Logging

Comprehensive logging system:

Daily log rotation in ./logs/ directory
Structured logging with timestamps and levels
Performance tracking integrated
Error tracking with stack traces

🤝 Contributing

Fork the repository
Create a feature branch
Add tests for new functionality
Ensure all tests pass
Submit a pull request

📄 License

MIT License - see LICENSE file for details.

🆘 Support

Issues: GitHub Issues
Documentation: README
Discussions: GitHub Discussions

🏷️ Version History

v1.2.0 - Enhanced MCP server with performance monitoring and health checks
v1.1.0 - Added semantic search and knowledge graph features
v1.0.0 - Initial release with basic memory and task management

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

An enhanced MCP server that provides intelligent memory and task management for AI assistants, featuring semantic search, automatic task extraction, and knowledge graphs to help manage development workflows.

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