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
🧬 Sequential Thinking Engine
- Structured reasoning chains with 5-stage process (analysis, planning, execution, validation, reflection)
- Context management with automatic token optimization
- Conversation continuity across sessions with intelligent summarization
- Real-time token estimation and compression (30-70% reduction)
- Auto-extraction of key points, decisions, and action items
📋 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
- Complex task decomposition into manageable subtasks
🏗️ Project Convention Learning
- Automatic environment detection - OS, shell, tools, and runtime versions
- Project type recognition - Node.js, Python, Rust, Go, Java, MCP servers, etc.
- Command pattern learning - Extracts npm scripts, Makefile targets, and project commands
- Tool configuration detection - IDEs, linters, CI/CD, build tools, and testing frameworks
- Dependency management - Package managers, lock files, and installation commands
- Smart command suggestions - Corrects user commands based on project conventions
- Windows-specific optimizations - Proper path separators and command formats
- Memory integration - Stores learned conventions for AI context and future reference
- 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
- Token usage analytics and optimization recommendations
� 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
- Token usage analytics and optimization recommendations
�🚀 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 with FastMCP integration
├── memory_manager.py # Core memory/task logic and project detection
├── sequential_thinking.py # Thinking chains and context optimization
├── database.py # Database operations with retry mechanisms
├── requirements.txt # Python dependencies
├── setup.py # Package configuration
├── data/ # SQLite database storage
├── logs/ # Application logs
🚀 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"
}
}
}
}
get_memory_context(query) - Get relevant memories and contextcreate_task(title, description, priority, category) - Create new tasksget_tasks(status, limit) - Retrieve tasks with filteringget_project_summary() - Get comprehensive project overview
start_thinking_chain(objective) - Begin structured reasoning processadd_thinking_step(chain_id, stage, title, content, reasoning) - Add reasoning stepsget_thinking_chain(chain_id) - Retrieve complete thinking chainlist_thinking_chains(limit) - List recent thinking chains
Context Management Tools
create_context_summary(content, key_points, decisions, actions) - Compress context for token optimizationstart_new_chat_session(title, objective, continue_from) - Begin new conversation with optional continuationconsolidate_current_session() - Compress current session for handoffget_optimized_context(max_tokens) - Get token-optimized contextestimate_token_usage(text) - Estimate token count for planning
Enterprise Auto-Processing
auto_process_conversation(content, interaction_type) - Extract memories and tasks automaticallydecompose_task(prompt) - Break complex tasks into subtasks
auto_learn_project_conventions(project_path) - Automatically detect and learn project patternsget_project_conventions_summary() - Get formatted summary of learned conventionssuggest_correct_command(user_command) - Suggest project-appropriate command correctionsremember_project_pattern(pattern_type, pattern, description) - Manually store project patternsupdate_memory_context() - Refresh memory context with latest project conventions
health_check() - Check server health and connectivityget_performance_stats() - Get detailed performance metricscleanup_old_data(days_old) - Clean up old memories and tasksoptimize_memories() - Remove duplicates and optimize storageget_database_stats() - Get comprehensive database statistics
🏗️ Project Convention Learning
The Enhanced MCP Memory Server automatically learns and remembers project-specific conventions to prevent AI assistants from suggesting incorrect commands or approaches:
Automatic Detection
- Operating System: Windows vs Unix, preferred shell and commands
- Project Type: Node.js, Python, Rust, Go, Java, MCP servers, FastAPI, Django
- Development Tools: IDEs, linters, formatters, CI/CD configurations
- Package Management: npm, yarn, pip, poetry, cargo, go modules
- Build Systems: Vite, Webpack, Make, batch scripts, shell scripts
Smart Command Suggestions
# Instead of generic commands, suggests project-specific ones:
User types: "node server.js"
AI suggests: "Use 'npm run dev' instead for this project"
User types: "python main.py"
AI suggests: "Use 'uvicorn main:app --reload' for this FastAPI project"
Windows Optimization
- Automatically detects Windows environment
- Uses
cmd.exe and Windows-appropriate path separators - Suggests Windows-compatible commands (e.g.,
dir instead of ls) - Handles Windows-specific Python and Node.js patterns
Memory Integration
All learned conventions are stored as high-importance memories that:
- Appear in AI context for every interaction
- Persist across sessions and project switches
- Include environment warnings and project-specific guidance
- Prevent repeated incorrect command suggestions
🔧 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 |
MAX_CONTEXT_TOKENS | 8000 | Token threshold for auto-compression |
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 |
DATA_DIR | ~/ClaudeMemory | Where to store data and logs |
🧪 Testing
This package is production-ready and does not include a test suite in the distributed version. For development or CI, refer to the repository for test scripts and additional resources.
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
Powered by sentence-transformers for intelligent memory retrieval:
- Natural language queries - "Find memories about database optimization"
- Similarity-based matching using embeddings
- Configurable similarity thresholds
- Automatic model downloading (~90MB on first run)
🧠 Sequential Thinking
Structured reasoning system:
- 5-stage thinking process: Analysis → Planning → Execution → Validation → Reflection
- Token optimization: Real-time estimation and compression (30-70% reduction)
- Context continuity: Intelligent session handoffs with preserved context
- Auto-extraction: Automatically identifies key points, decisions, and action items
- Performance tracking: Monitor reasoning chains and optimization metrics
💼 Token Management
Advanced context optimization for high-scale deployments:
- Smart compression: Pattern-based extraction preserves essential information
- Token estimation: Real-time calculation for planning and budgeting
- Context summarization: Automatic conversion of conversations to actionable summaries
- Session consolidation: Seamless handoffs between conversation sessions
- Performance analytics: Detailed metrics on compression ratios and response times
📝 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
🏷️ Version History
- v2.0.2 - Updated package build configuration and license compatibility fixes
- v2.0.1 - Enhanced features with sequential thinking and project conventions
- 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