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Version 1.1.0 - Enterprise-grade PostgreSQL operations with 63 tools, 10 resources, and 10 prompts
- Quick Start Guide - Get up and running in 30 seconds
- Installation & Configuration - Detailed setup instructions
- Extension Setup - PostgreSQL extensions (pg_stat_statements, hypopg, pgvector, PostGIS)
- MCP Resources & Prompts - NEW! Intelligent meta-awareness and guided workflows
- Core Database Tools (9 tools) - Schema management, SQL execution, health monitoring
- JSON Operations (15 tools) - JSONB operations, validation, security scanning
- Text Processing (6 tools) - Similarity search, full-text search, fuzzy matching
- Statistical Analysis (8 tools) - Descriptive stats, correlation, regression
- Performance Intelligence (6 tools) - Query optimization, workload analysis
- Vector & Semantic Search (8 tools) - Embeddings, similarity search, clustering
- Geospatial Operations (7 tools) - Spatial queries, distance calculation, GIS
- Backup & Recovery (4 tools) - Backup planning, restore validation
- Monitoring & Alerting (5 tools) - Real-time monitoring, capacity planning
- Security & Best Practices - SQL injection prevention, security modes
- Troubleshooting - Common issues and solutions
- MCP Configuration - Claude Desktop, Cursor, and other MCP clients
The PostgreSQL MCP Server provides 63 specialized tools, 10 intelligent resources, and 10 guided prompts for comprehensive database operations:
| Category | Tools | Key Features |
|---|---|---|
| Core Database | 9 | Schema management, SQL execution, health monitoring |
| JSON Operations | 15 | JSONB, validation, security scanning, diff/merge |
| Text Processing | 6 | Trigram similarity, full-text search, fuzzy matching |
| Statistical Analysis | 8 | Descriptive stats, correlation, time series |
| Performance | 6 | Query optimization, index tuning, workload analysis |
| Vector Search | 8 | Semantic search, embeddings, clustering |
| Geospatial | 7 | PostGIS, spatial queries, coordinate transformation |
| Backup & Recovery | 4 | Logical/physical backup, restore validation |
| Monitoring | 5 | Real-time metrics, alerting, capacity planning |
Real-time database meta-awareness that AI can access automatically:
- Database Schema - Instant access to all tables, columns, types
- Database Capabilities - Available extensions, features, pg_stat_statements
- Performance Metrics - Top queries, cache hit rates, slow queries
- Database Health - Connection pool, indexes, vacuum status
- Extension Status - pgvector, PostGIS, hypopg availability
- Index Statistics - Usage, size, recommendations
- Connection Pool - Active connections, utilization
- Replication Status - Lag, health monitoring
- Vacuum Status - Bloat, last vacuum/analyze times
- Lock Information - Active locks, blocking queries
Guided workflows for complex operations:
- Optimize Query - Step-by-step query optimization
- Index Tuning - Comprehensive index recommendations
- Database Health Check - Full health assessment workflow
- Setup pgvector - Complete vector search setup
- Setup PostGIS - Complete geospatial setup
- JSONB Best Practices - JSONB optimization guide
- Performance Baseline - Establish performance baselines
- Backup Strategy - Comprehensive backup planning
- Extension Setup - Step-by-step extension installation
- Query Analysis - Deep dive query analysis
- Zero Known Vulnerabilities - Comprehensive security audit passed
- SQL Injection Prevention - Parameter binding with automatic sanitization
- Dual Security Modes - Restricted (production) and unrestricted (development)
- Enterprise-Grade Protection - Query validation, audit logging
- Real-Time Analytics - pg_stat_statements integration
- Hypothetical Index Testing - HypoPG for zero-risk optimization
- DTA Algorithm - Microsoft SQL Server-inspired index tuning
- Buffer Cache Analysis - 99%+ accuracy monitoring
- Schema Intelligence - Context-aware SQL generation
- Query Optimization - Automated performance improvements
- Predictive Analysis - Performance forecasting
- Natural Language Interface - Human-friendly database interactions
- PostgreSQL 13-17 - Full version compatibility
- Multi-Platform - Windows, Linux, macOS (amd64, arm64)
- 100% Type Safe - Pyright strict mode (2,000+ issues resolved)
- Zero Linter Errors - Clean codebase with comprehensive type checking
- CI/CD Ready - Automated testing and security validation
- ✅ NEW: MCP Resources (10) - Real-time database meta-awareness
- ✅ NEW: MCP Prompts (10) - Guided workflows for complex operations
- ✅ Intelligent Assistant - Transforms from tool collection to database expert
- ✅ Pyright Strict Mode - 2,000+ type issues resolved, 100% type-safe codebase
- ✅ Zero Linter Errors - Clean codebase with comprehensive type checking
- ✅ Zero Breaking Changes - All existing tools work unchanged
- ✅ Production Ready: Enterprise-grade PostgreSQL MCP server
- ✅ 63 Specialized Tools: Complete feature set across 9 categories
- ✅ Zero Known Vulnerabilities: Comprehensive security audit passed
- ✅ Type Safety: Pyright strict mode compliance
- ✅ Multi-Platform Support: Windows, Linux, macOS (amd64, arm64)
- ✅ Backup & Recovery Suite: 4 new tools implemented
- ✅ Monitoring & Alerting Suite: 5 new tools implemented
- ✅ All 63 Tools Operational: Complete Phase 5 implementation
- ✅ Code Quality: Ruff formatting and linting passing
- ✅ Vector/Semantic Search: 8 tools with pgvector integration
- ✅ Geospatial Operations: 7 tools with PostGIS integration
- ✅ Extension Support: pgvector v0.8.0 and PostGIS v3.5.0
- ✅ Graceful Degradation: Informative errors for missing extensions
This wiki is organized to help you find information quickly:
- Start with Quick Start
- Review Installation & Configuration
- Set up Extensions
- Configure your MCP Client
- Explore Core Database Tools
- Learn JSON Operations
- Master Performance Intelligence
- Understand Security Best Practices
- Review Backup & Recovery
- Set up Monitoring & Alerting
- Optimize with Performance Intelligence
- Implement Security Best Practices
- GitHub Repository - Source code and issues
- Security Policy - Vulnerability reporting
- Contributing Guide - Development guidelines
- Docker Hub - Container images
- PyPI Package - Python package
execute_sql(
sql="SELECT * FROM users WHERE id = %s",
params=[123]
)analyze_db_health(health_type="all")monitor_real_time(
include_queries=True,
include_locks=True,
include_io=True
)vector_similarity(
table_name="documents",
vector_column="embedding",
query_vector=[0.1, 0.2, ...],
distance_metric="cosine",
limit=10
)- Version 1.1.0 - AI-native intelligence release (October 4, 2025)
- 63 MCP Tools across 9 categories
- 10 MCP Resources for database meta-awareness
- 10 MCP Prompts for guided workflows
- 7,500+ lines of implementation code
- 14 modules with specialized functionality
- 100% Type Safe - Pyright strict mode (2,000+ issues resolved)
- Zero Linter Errors - Clean codebase with comprehensive type checking
- Zero Vulnerabilities - Comprehensive security audit passed
- Issues: GitHub Issues
- Security: admin@adamic.tech
- Discussions: GitHub Discussions
- Contributing: See Contributing Guide
Version 1.1.0 - Last Updated: October 4, 2025