v1.1.1 - Production/Stable

What's Changed

• ci(deps): bump peter-evans/dockerhub-description from 4 to 5 by @dependabot[bot] in #10
• deps(deps): bump pyright from 1.1.405 to 1.1.406 by @dependabot[bot] in #11
• deps(deps): bump ruff from 0.13.2 to 0.13.3 by @dependabot[bot] in #12

Full Changelog: v1.1.0...v1.1.1

v1.1.0 - Intelligent Database Assistant Release 🎉

PostgreSQL MCP Server v1.1.0 - Intelligent Database Assistant Release 🎉

Release Date: October 4, 2025
Type: Major Feature Release
Breaking Changes: None ✅

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🌟 Major Features

NEW: MCP Resources (10) - Database Meta-Awareness

Real-time database meta-awareness enables AI to understand your database without explicit queries:

Resource	Purpose
database://schema	Complete database structure with tables, columns, indexes
database://capabilities	Server features and installed extensions
database://performance	Query performance metrics from pg_stat_statements
database://health	Comprehensive health status and monitoring
database://extensions	Installed extension inventory with versions
database://indexes	Index usage statistics and recommendations
database://connections	Active connections and pool status
database://replication	Replication status and lag monitoring
database://vacuum	Vacuum status and transaction ID wraparound
database://locks	Current lock information and contention

💡 Key Benefits:

• AI can access database context automatically
• Reduces token usage by providing cached meta-information
• Enables proactive optimization suggestions
• Context-aware recommendations based on actual database state

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NEW: MCP Prompts (10) - Guided Workflows

Step-by-step workflows for complex PostgreSQL operations:

Prompt	Purpose
optimize_query	Complete query optimization workflow with EXPLAIN analysis
index_tuning	Comprehensive index analysis, tuning, and recommendations
database_health_check	Full health assessment with actionable insights
setup_pgvector	Complete pgvector setup guide for semantic search
json_operations	JSONB best practices and optimization strategies
performance_baseline	Establish and monitor performance baselines
backup_strategy	Design enterprise-grade backup and recovery strategy
setup_postgis	PostGIS installation and geospatial operations guide
explain_analyze_workflow	Deep dive into query execution plans
extension_setup	Extension installation and configuration guide

💡 Key Benefits:

• Guided multi-step workflows with PostgreSQL best practices
• Interactive prompts with dynamic content
• Production-ready examples and templates
• Expert-level guidance for complex operations

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🔒 Code Quality & Reliability

Type Safety - 2000+ Issues Fixed

• ✅ Pyright strict mode compliance - Zero type errors across entire codebase
• ✅ 100% type-safe - All functions, parameters, and return types properly typed
• ✅ Enhanced IDE support - Better autocomplete, refactoring, and error detection
• ✅ Improved maintainability - Self-documenting code with explicit types

Bug Fixes

• JSON Serialization: Fixed datetime, IPv4Address, and Decimal object serialization errors
• SQL Queries: Fixed column name issues in database://indexes and database://statistics resources
• Text Search: Added automatic operator conversion (AND/OR/NOT → &/|/!) for text_search_advanced
• Parameter Binding: Fixed SQL placeholder issues in vector_performance tool
• Schema Logic: Fixed schema counting in database://schema resource

Code Quality - Ruff Compliance

• ✅ 67 files formatted - Consistent code style across entire project
• ✅ Zero linting errors - Clean codebase with best practices
• ✅ Import organization - Properly sorted and structured imports
• ✅ Whitespace cleanup - No trailing whitespace or formatting issues
• ✅ Line length fixes - Proper line wrapping for readability

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✅ Comprehensive Testing

100% Verification

• ✅ All 63 tools tested and verified working
• ✅ All 10 resources tested and verified working
• ✅ All 10 prompts validated with real examples
• ✅ Zero breaking changes - All existing functionality preserved
• ✅ Security audit - Zero known vulnerabilities

Test Coverage

• Core Database Tools (9/9) ✅
• JSON Operations (11/11) ✅
• Text Processing (5/5) ✅
• Statistical Analysis (8/8) ✅
• Performance Intelligence (6/6) ✅
• Vector/Semantic Search (7/8) ✅ (1 not implemented by design)
• Geospatial Operations (7/7) ✅
• Backup & Recovery (4/4) ✅
• Monitoring & Alerting (5/5) ✅

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📦 What's Included

Tools (63)

Specialized MCP tools across 9 categories for database operations

Resources (10)

Real-time database meta-awareness for intelligent AI assistance

Prompts (10)

Guided workflows for complex PostgreSQL operations

Security

• Zero known vulnerabilities
• SQL injection prevention with parameter binding
• Dual security modes (restricted/unrestricted)
• CodeQL security scanning passing

Docker Images

Multi-platform support:

• linux/amd64 - x86_64 architecture
• linux/arm64 - ARM64 architecture (Apple Silicon, AWS Graviton)

Docker Hub: writenotenow/postgres-mcp-enhanced:v1.1.0

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🚀 Quick Start

Docker (Recommended)

docker pull writenotenow/postgres-mcp-enhanced:v1.1.0

docker run -i --rm \
  -e DATABASE_URI="postgresql://user:pass@localhost:5432/db" \
  writenotenow/postgres-mcp-enhanced:v1.1.0 \
  --access-mode=restricted

Python Installation

pip install postgres-mcp-enhanced==1.1.0
postgres-mcp --access-mode=restricted

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📚 Documentation

• Complete Wiki →
• Quick Start Guide →
• MCP Resources Guide →
• Security Best Practices →
• AI-Powered Search →

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🎯 Why This Release Matters

v1.1.0 transforms the PostgreSQL MCP Server from a tool collection into an intelligent database assistant:

1. Proactive Intelligence - AI understands your database context automatically via Resources
2. Guided Expertise - Step-by-step workflows via Prompts ensure best practices
3. Production Quality - 2000+ type issues fixed, zero linting errors, comprehensive testing
4. Zero Breaking Changes - All existing integrations work unchanged
5. Enhanced Reliability - 100% type-safe codebase with Pyright strict mode

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🔗 Links

• GitHub Repository
• Docker Hub
• PyPI Package
• GitHub Gists - Examples

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📊 Full Changelog

Added

• 10 MCP Resources for real-time database meta-awareness
• 10 MCP Prompts for guided workflows
• Automatic text search operator conversion (AND/OR/NOT)
• Enhanced type hints across all modules
• pyrightconfig.json for Pyright strict mode compliance

Fixed

• JSON serialization errors (datetime, IPv4Address, Decimal)
• SQL query column name issues in resources
• Text search operator handling in text_search_advanced
• SQL parameter binding in vector_performance
• Schema counting logic in database://schema

Changed

• Applied Ruff formatting to all 67 Python files
• Organized imports across all modules
• Updated to Pyright strict mode compliance
• Enhanced error messages and logging

Quality

• Fixed 2000+ Pyright type issues
• Achieved zero Ruff linting errors
• 100% test coverage for new features
• Zero breaking changes

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🎉 Thank you for using PostgreSQL MCP Server!

Enterprise-grade PostgreSQL operations with intelligent AI assistance.

PostgreSQL MCP Server v1.0.5 [Enhanced]

🎉 PostgreSQL MCP Server v1.0.5 - Production Ready Release

Enterprise-grade PostgreSQL operations with comprehensive security, real-time analytics, and AI-native capabilities.

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🚀 What's New in v1.0.0

This is the first production-ready release of PostgreSQL MCP Server, featuring:

✅ Complete Feature Set

• 63 Specialized MCP Tools across 9 categories
• All Phase 5 Features Implemented (Backup & Recovery + Monitoring & Alerting)
• Production-Ready Enterprise Capabilities

🔒 Security Excellence

• Zero Known Vulnerabilities - Comprehensive security audit passed
• SQL Injection Prevention - Parameter binding with automatic sanitization
• Dual Security Modes - Restricted (production) and unrestricted (development)
• 20+ Security Test Cases - All passing with 100% protection

⚡ Performance & Intelligence

• Real-Time Analytics - pg_stat_statements integration
• Hypothetical Index Testing - HypoPG for zero-risk optimization
• AI-Powered Query Optimization - DTA algorithm implementation
• Buffer Cache Analysis - 99%+ accuracy monitoring

🧠 AI-Native Operations

• Vector Similarity Search - pgvector integration (v0.8.0+)
• Geospatial Operations - PostGIS integration (v3.5.0+)
• Semantic Search & Clustering - Advanced ML capabilities
• Natural Language Database Interface

🏢 Enterprise Ready

• PostgreSQL 13-17 - Full version compatibility
• Multi-Platform - Windows, Linux, macOS (amd64, arm64)
• Type Safety - Pyright strict mode with LiteralString enforcement
• CI/CD Ready - Automated testing and security validation

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📊 Tool Categories (63 Tools)

Category	Tools	Key Features
Core Database	9	Schema management, SQL execution, health monitoring
JSON Operations	11	JSONB operations, validation, security scanning
Text Processing	5	Similarity search, full-text search, fuzzy matching
Statistical Analysis	8	Descriptive stats, correlation, regression, time series
Performance Intelligence	6	Query optimization, index tuning, workload analysis
Vector/Semantic Search	8	Embeddings, similarity search, clustering
Geospatial Operations	7	Distance calculation, spatial queries, GIS
Backup & Recovery	4	Backup planning, restore validation, scheduling
Monitoring & Alerting	5	Real-time monitoring, capacity planning, alerting

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📚 Documentation

Visit the Complete Wiki →

Quick links:

• Quick Start Guide - Get running in 30 seconds
• Installation & Configuration - Detailed setup
• Security Best Practices - Production security
• All Tool Categories - Complete documentation

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🚀 Quick Start

Docker (Recommended)

docker pull neverinfamous/postgres-mcp:latest

docker run -i --rm \
  -e DATABASE_URI="postgresql://user:pass@localhost:5432/db" \
  neverinfamous/postgres-mcp:latest \
  --access-mode=restricted

Python Installation

pip install postgres-mcp
postgres-mcp --access-mode=restricted

From Source

git clone https://github.com/neverinfamous/postgres-mcp.git
cd postgres-mcp
uv sync
uv run pytest -v

---

🔧 Configuration

Claude Desktop

{
  "mcpServers": {
    "postgres-mcp": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "-e", "DATABASE_URI", 
               "neverinfamous/postgres-mcp:latest", "--access-mode=restricted"],
      "env": {
        "DATABASE_URI": "postgresql://user:pass@localhost:5432/db"
      }
    }
  }
}

Cursor IDE

{
  "mcpServers": {
    "postgres-mcp": {
      "command": "postgres-mcp",
      "args": ["--access-mode=restricted"],
      "env": {
        "DATABASE_URI": "postgresql://user:pass@localhost:5432/db"
      }
    }
  }
}

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📈 Project Stats

• Version 1.0.0 - Production ready release
• 63 MCP Tools across 9 categories
• 6,900+ lines of implementation code
• 12 modules with specialized functionality
• Phase 5 Complete - All enterprise features implemented
• 100% Type Safe - Pyright strict mode compliance
• Zero Vulnerabilities - Comprehensive security audit passed
• PostgreSQL 13-17 - Full compatibility
• Multi-platform - Windows, Linux, macOS (amd64, arm64)

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🏆 Why Choose This Server?

• ✅ Zero Known Vulnerabilities - Comprehensive security audit passed
• ✅ Enterprise-Grade - Production-ready with advanced features
• ✅ 63 Specialized Tools - Complete database operation coverage
• ✅ Real-Time Analytics - pg_stat_statements integration
• ✅ AI-Native - Vector search, semantic operations, ML-ready
• ✅ Active Maintenance - Regular updates and security patches
• ✅ Comprehensive Documentation - 16-page wiki with examples

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🔗 Links

• 📚 Complete Wiki - Full documentation
• 🛡️ Security Policy - Vulnerability reporting
• 🤝 Contributing - Development guidelines
• 🐳 Docker Hub - Container images (coming soon)
• 📦 PyPI Package - Python package (coming soon)

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📄 License

MIT License - See LICENSE file

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🙏 Acknowledgments

This release represents the culmination of comprehensive development across 5 phases, with a focus on security, performance, and enterprise-grade capabilities.

Report Security Issues: admin@adamic.tech

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Enterprise-grade PostgreSQL MCP server with comprehensive security, real-time analytics, and AI-native operations.