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\nAdvanced PostgreSQL Database Analysis & Optimization Suite
\nExtended version based on crystaldba/postgres-mcp\n

\n\n## 🚀 Key Features\n\n- **🔍 Comprehensive Database Analysis**: Deep insights into schema structure, relationships, and performance\n- **⚡ AI-Powered Optimization**: Intelligent index recommendations using Database Tuning Advisor (DTA) and LLM methods\n- **🩺 Advanced Health Monitoring**: Multi-dimensional health checks with predictive analytics\n- **🔒 Lock & Blocking Analysis**: Real-time detection and resolution of query blocking and deadlocks\n- **🧹 Smart Maintenance**: Automated vacuum analysis with bloat detection and maintenance scheduling\n- **📊 Performance Intelligence**: Query performance analysis with resource usage optimization\n- **🔐 Security Assessment**: Comprehensive security analysis and recommendations\n- **🐳 Docker Ready**: Containerized deployment with Docker Compose support\n\n## 📋 Available Tools\n\n### Core Database Operations\n| Tool Name | Description |\n|-----------|-------------|\n| `list_schemas` | List all schemas with ownership and type classification |\n| `list_objects` | Browse database objects (tables, views, sequences, extensions) by schema |\n| `get_object_details` | Detailed object analysis including columns, constraints, and indexes |\n| `execute_sql` | Execute SQL with safety controls (restricted/unrestricted modes) |\n\n### Performance & Optimization\n| Tool Name | Description |\n|-----------|-------------|\n| `explain_query` | Advanced execution plan analysis with HypoPG hypothetical index simulation |\n| `get_top_queries` | Identify slow and resource-intensive queries with performance metrics |\n| `analyze_workload_indexes` | AI-powered index recommendations from workload analysis (DTA/LLM) |\n| `analyze_query_indexes` | Targeted index optimization for specific query sets (up to 10 queries) |\n\n### Health & Monitoring\n| Tool Name | Description |\n|-----------|-------------|\n| `analyze_db_health` | Comprehensive health checks: indexes, connections, vacuum, sequences, replication, buffer cache, constraints |\n| `get_blocking_queries` | Advanced blocking analysis with lock hierarchy visualization and resolution recommendations |\n| `analyze_vacuum_requirements` | Comprehensive vacuum analysis with bloat detection and maintenance recommendations |\n\n### Advanced Analysis\n| Tool Name | Description |\n|-----------|-------------|\n| `get_database_overview` | Enterprise-grade database assessment with performance, security, and relationship analysis |\n| `analyze_schema_relationships` | Schema dependency mapping with visual relationship analysis and coupling metrics |\n\n## 🔧 Tool Details & Capabilities\n\n### 🔍 Database Overview Analysis\n**Enterprise-grade comprehensive database assessment**\n\nThe `get_database_overview` tool provides multi-dimensional analysis:\n\n- **📊 Schema Analysis**: Complete structure with table relationships and dependency mapping\n- **⚡ Performance Metrics**: Query performance, index efficiency, and resource utilization patterns\n- **🔐 Security Analysis**: User permissions, role assignments, and security configuration assessment\n- **💾 Storage Analysis**: Table sizes, index bloat detection, and disk usage optimization\n- **🩺 Health Indicators**: Connection health, vacuum statistics, and system performance metrics\n\n**Configuration Options:**\n- `max_tables` (default: 500): Maximum tables to analyze per schema for performance control\n- `sampling_mode` (default: true): Statistical sampling for large datasets to optimize execution time\n- `timeout` (default: 300): Maximum execution time with graceful timeout handling\n\n### 🔒 Advanced Blocking Queries Analysis\n**Real-time lock contention detection and resolution**\n\nThe `get_blocking_queries` tool features enterprise-grade capabilities:\n\n**🎯 Core Features:**\n- **Modern Detection**: Uses PostgreSQL's `pg_blocking_pids()` function for accurate blocking identification\n- **Lock Hierarchy Visualization**: Complete blocking chains and process relationships\n- **Comprehensive Metrics**: Process details, wait events, timing, lock types, and affected relations\n- **Intelligent Recommendations**: Severity-based suggestions with specific optimization guidance\n- **Production Ready**: Designed for enterprise database monitoring and performance troubleshooting\n\n**📋 Analysis Output:**\n- **Process Information**: PID, user, application name, client address, and connection details\n- **Query Context**: Full query text, execution timing, and resource consumption\n- **Lock Details**: Lock types, modes, affected database objects, and wait events\n- **State Analysis**: Process states, wait information, and blocking duration\n- **Trend Analysis**: Summary statistics and pattern recognition\n- **Categorized Recommendations**: 🚨 Critical, ⚠️ Warning, 💡 Optimization, 🎯 Hotspot alerts\n\n**🔧 PostgreSQL Compatibility:**\n- **Minimum**: PostgreSQL 9.6+ (requires `pg_blocking_pids()` function)\n- **Recommended**: PostgreSQL 12+ (enhanced lock monitoring features)\n- **Optimal**: PostgreSQL 14+ (includes `pg_locks.waitstart` for precise wait timing)\n\n### 🧹 Vacuum Analysis & Maintenance\n**Comprehensive maintenance planning with bloat detection**\n\nThe `analyze_vacuum_requirements` tool provides:\n\n- **📈 Bloat Analysis**: Table and index bloat detection with severity assessment\n- **⚙️ Autovacuum Configuration**: Settings analysis and optimization recommendations\n- **📊 Performance Impact**: Vacuum operation performance analysis and bottleneck identification\n- **🗓️ Maintenance Planning**: Intelligent scheduling recommendations based on workload patterns\n- **🚨 Critical Issue Detection**: Immediate attention alerts for maintenance-related problems\n- **⚡ Configuration Optimization**: Tuning suggestions for vacuum parameters\n\n### 🗺️ Schema Relationship Analysis\n**Advanced dependency mapping and visualization**\n\nThe `analyze_schema_relationships` tool offers:\n\n- **🔗 Dependency Mapping**: Complete inter-schema relationship visualization\n- **📊 Coupling Analysis**: Schema coupling metrics and isolation scoring\n- **🎯 Impact Assessment**: Change impact analysis for schema modifications\n- **📈 Relationship Quality**: Foreign key relationship quality and consistency scoring\n- **🔍 Pattern Detection**: Common anti-patterns and architectural recommendations\n\n### ⚡ Index Optimization Intelligence\n**AI-powered index recommendations with advanced algorithms**\n\n**Database Tuning Advisor (DTA) Features:**\n- **🧠 Pareto Optimization**: Multi-objective optimization balancing performance and storage\n- **📊 Workload Analysis**: Pattern recognition from pg_stat_statements data\n- **💰 Cost-Benefit Analysis**: Storage budget constraints with performance impact assessment\n- **🎯 Query-Specific Tuning**: Targeted optimization for specific query sets\n- **⏱️ Time-bounded Analysis**: Anytime algorithm with configurable runtime limits\n\n**LLM-Powered Optimization:**\n- **🤖 Intelligent Analysis**: Natural language understanding of query patterns\n- **📝 Contextual Recommendations**: Human-readable explanations with implementation guidance\n- **🔍 Advanced Pattern Recognition**: Complex query pattern detection and optimization\n\n## 🚀 Quick Start\n\n### Prerequisites\n- PostgreSQL 9.6+ (PostgreSQL 12+ recommended, 14+ optimal)\n- Python 3.8+\n- Optional: HypoPG extension for hypothetical index analysis\n\n### Installation & Setup\n\n#### 1. Environment Configuration\nCreate a `.env` file in the project root:\n\n```bash\nDATABASE_URI=postgresql://username:password@localhost:5432/database_name\n```\n\n#### 2. Native Deployment\n```bash\n# Start the MCP server (default: stdio transport, unrestricted mode)\n./start.sh\n\n# Start in read-only mode for safer analysis\n./start.sh --access-mode restricted\n\n# Start with SSE transport for web integration\n./start.sh --transport sse --sse-port 8099\n\n# Start SSE server accessible externally\n./start.sh --transport sse --sse-host 0.0.0.0 --sse-port 8099\n\n# Show all available options\n./start.sh --help\n```\n\n#### 3. Docker Deployment\n```bash\n# Start with Docker Compose\ndocker-compose up -d\n\n# View logs\ndocker-compose logs -f postgres-mcp\n```\n\n#### 4. Interactive Testing (MCP Inspector)\n```bash\n# Terminal 1: Start the MCP server with SSE transport\n./start.sh --transport sse --sse-port 8099\n\n# Terminal 2: Start the MCP Inspector (opens web interface)\n./start-inspector.sh\n```\n\nThe MCP Inspector provides:\n- **Interactive Tool Testing**: Test all database analysis tools with a web UI\n- **Parameter Exploration**: Discover tool capabilities and configuration options \n- **Real-time Results**: View formatted analysis results in a user-friendly interface\n- **Documentation**: Built-in tool documentation and usage examples\n\n### 🔧 Access Modes\n\n**Unrestricted Mode** (Default):\n- Full SQL execution capabilities\n- Database modification operations\n- Complete administrative access\n\n**Restricted Mode** (Recommended for analysis):\n- Read-only operations with safety controls\n- SQL injection protection\n- Timeout enforcement (30s default)\n- Safe for production analysis\n\n### 📊 Usage Examples\n\n#### Basic Server Operations\n```bash\n# Show help and configuration options\n./start.sh --help\n\n# Start with default settings (stdio, unrestricted)\n./start.sh\n\n# Start in production-safe mode\n./start.sh --access-mode restricted\n\n# Start web server for HTTP/SSE integration\n./start.sh --transport sse --sse-port 8099\n```\n\n#### Health Check Examples\n```bash\n# Comprehensive health analysis (via MCP client)\nanalyze_db_health --health-type all\n\n# Specific component checks\nanalyze_db_health --health-type index,vacuum,buffer\n\n# Performance optimization workflow\nget_top_queries --sort-by resources\nanalyze_workload_indexes --method dta --max-index-size-mb 1000\nget_blocking_queries\n```\n\n## 🏗️ Architecture & Components\n\n### Core Architecture\n```\npostgres-mcp/\n├── 🔧 server.py # MCP server & tool registration\n├── 📊 database_health/ # Multi-dimensional health monitoring\n├── ⚡ explain/ # Query execution plan analysis\n├── 🎯 index/ # AI-powered index optimization\n├── 📈 top_queries/ # Performance query analysis\n├── 🔒 blocking_queries.py # Lock contention analysis\n├── 🔍 database_overview.py # Comprehensive assessment\n├── 🗺️ schema_mapping.py # Relationship visualization\n├── 🧹 vacuum_analysis.py # Maintenance optimization\n└── 🛡️ sql/ # SQL execution framework\n```\n\n### Database Health Components\n- **Index Health**: Invalid, duplicate, bloated, and unused index detection\n- **Connection Health**: Connection utilization and capacity analysis \n- **Vacuum Health**: Transaction wraparound and maintenance monitoring\n- **Sequence Health**: Sequence exhaustion and overflow protection\n- **Replication Health**: Lag monitoring and slot management\n- **Buffer Health**: Cache hit rate optimization for tables and indexes\n- **Constraint Health**: Invalid constraint detection and remediation\n\n### 🤖 AI Integration Features\n\n**Database Tuning Advisor (DTA):**\n- Pareto-optimal index selection algorithm\n- Multi-query workload optimization\n- Budget-constrained recommendation engine\n- Time-bounded analysis with anytime approach\n\n**LLM-Powered Analysis:**\n- Natural language query pattern understanding\n- Contextual optimization recommendations \n- Human-readable explanations and guidance\n- Advanced pattern recognition capabilities\n\n## 📈 Recent Enhancements\n\n### Latest Features (Recent Commits)\n- ✅ **Comprehensive Tool Analysis**: Detailed analysis document with improvement recommendations\n- ✅ **Enhanced Readability**: Streamlined code formatting across all modules\n- ✅ **Robust Error Handling**: Improved None value handling in vacuum analysis\n- ✅ **Advanced Visualizations**: Enhanced blocking queries analysis with detailed recommendations\n- ✅ **Human-Readable Outputs**: Refactored analysis tools for better text presentation\n- ✅ **Schema Relationship Mapping**: New schema dependency analysis and visualization\n- ✅ **Docker Integration**: Complete containerization with Docker Compose support\n- ✅ **Vacuum Analysis Tool**: Comprehensive maintenance recommendations and bloat detection\n\n### Architecture Improvements\n- **Modular Design**: Enhanced component separation and reusability\n- **Async Optimization**: Improved performance with better async patterns\n- **Safety Framework**: Comprehensive SQL execution safety controls\n- **Error Recovery**: Robust error handling and graceful degradation\n- **Performance Scaling**: Optimized for large database analysis\n- **Enhanced Startup Scripts**: Flexible configuration with comprehensive validation and help system\n\n## 📚 Documentation & Development\n\n### Advanced Documentation\n- **[Database Tools Analysis](plan/database-tools-analysis.md)**: Comprehensive analysis of all tools with improvement recommendations\n- **[Tool Improvements Roadmap](todos/tool_improvements.md)**: Priority-based enhancement roadmap *(if available)*\n- **Technical Implementation**: Detailed code documentation and API references\n\n### Extension Points\n- **Custom Health Checks**: Add domain-specific health monitoring\n- **Plugin Architecture**: Extend with custom analysis tools\n- **Integration APIs**: Connect with external monitoring systems\n- **Custom Visualizations**: Add specialized reporting and dashboards\n\n## 🔒 Security & Best Practices\n\n### Security Features\n- **SQL Injection Protection**: Comprehensive input sanitization\n- **Access Mode Controls**: Restricted/unrestricted operation modes \n- **Timeout Enforcement**: Configurable query timeout protection\n- **Parameter Validation**: Robust input validation and sanitization\n- **Error Handling**: Secure error reporting without information leakage\n\n### Production Guidelines\n- Use **restricted mode** for production analysis\n- Configure appropriate **timeout values** for large operations\n- Monitor **resource usage** during analysis operations\n- Implement **regular health checks** for proactive monitoring\n- Review **security configurations** and user permissions regularly\n\n## 📄 License\n\nMIT License\n\n---\n\n

\n🚀 Postgres MCP Pro Plus - Advanced Database Intelligence
\nEmpowering database professionals with AI-driven insights and optimization\n

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