Database

guyinwonder1681stars

Production-ready MCP provider for SQL databases with unified AI agent interface, supporting MySQL, MariaDB, PostgreSQL, and SQLite with secure credential management and comprehensive query tools.

Databases

Install Run on PlugMCP

GitHub

Run on PlugMCP

1# Database

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-28

Stars: 1
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Database?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Database need an API key?

No required environment variable is listed on this record.

What transport does Database use?

Database runs locally over stdio.

When was Database last checked?

Last checked 2026-10-28. Production-ready MCP provider for SQL databases with unified AI agent interface, supporting MySQL, MariaDB, PostgreSQL, and SQLite with secure credential management and comprehensive query tools.

From the publisher

Documentation

Database MCP Server

A production-ready Model Context Protocol (MCP) provider for SQL databases, built using various vibe coding tools. Supports MySQL, MariaDB, PostgreSQL, and SQLite. Features robust connection pooling, secure AES-GCM credential storage, structured JSON logging, comprehensive schema introspection, and a full suite of 21 MCP tools. Built and tested with Go 1.26.0.

🚀 Quick Start

# Clone the repository
git clone https://github.com/guyinwonder168/database-mcp-server.git
cd database-mcp-server

# Build the server
go build -o mcp-server ./cmd/server/main.go

# Run the server
./mcp-server

📦 Container Package (GHCR)

# Pull the release image
docker pull ghcr.io/guyinwonder168/database-mcp-server:v1.6.3

# Run with stdio transport
docker run --rm -i ghcr.io/guyinwonder168/database-mcp-server:v1.6.3

# Persist config.yaml and logs on host
mkdir -p ./.mcp-data
docker run --rm -i \
  -v "$(pwd)/.mcp-data:/app" \
  ghcr.io/guyinwonder168/database-mcp-server:v1.6.3

Package registry: https://github.com/guyinwonder168/database-mcp-server/pkgs/container/database-mcp-server

📋 Features

🔧 Interactive Setup - Auto-creates config.yaml if missing; all configuration is managed via MCP actions
👥 Profile Management - Create, update, delete, or clone database profiles via MCP
⚡ SQL Execution - Run arbitrary SQL queries (with read-only enforcement)
🔍 Schema Introspection - List tables/views with schema info, describe table schemas, list databases, discover joins, and browse schemas
🗂️ Multi-Schema Support - Work with tables in any PostgreSQL schema with automatic detection and schema-qualified queries
📊 Sample Data Fetching - Fetch sample rows to infer data formats and value ranges
🔗 Automated Join Discovery - Suggest JOIN SQL for building complex queries
🚦 Query Optimization - EXPLAIN-based analysis with findings and performance estimates
🛡️ Query Validation - Syntax, logic, and security checks before execution
🤖 Smart Query Builder - Generate SQL queries programmatically (integrated into analyze-schema AIQuerySuggestions)
🔒 Read-only Profiles - Prevent write operations on selected profiles
🔐 Secure Credentials - Passwords are encrypted at rest using AES-GCM (256-bit)
🏊 Connection Pooling - Efficient, configurable pooling with max pool size
📝 Structured Logging & Error Handling - All actions and errors are logged as structured JSON; actionable error responses
🛠️ Tool Discovery - list-tools MCP action returns a machine-readable list of all available tools/actions
🔌 Official MCP Protocol - Communication via stdio (not HTTP server; JSON is exchanged over stdio via official Go MCP SDK)
📈 Business Intelligence - Discover KPIs, trends, anomalies, and distribution patterns via discover-insights
🧭 Data Lineage - Analyze upstream/downstream dependencies via analyze-data-lineage
🧱 Schema Evolution Tracking - Track schema snapshots, detect drift, and generate migration scripts via track-schema-changes
🧬 Advanced Data Profiling - Optional statistical/pattern profiling for analyze-schema via profiling: true
🌐 Multi-Database Federation - Execute federated subqueries with cross-profile joins via federated-query

🛠️ Supported MCP Tools

Tool	Description
`configure-profile`	Create, update, delete, or clone database connection profiles
`list-profiles`	List all configured database profiles
`execute-sql`	Execute arbitrary SQL queries with read-only enforcement
`list-tables`	List tables with schema information in selected database
`describe-table`	Describe comprehensive table schema with metadata (supports schema parameter)
`list-databases`	List accessible databases for profile
`list-schemas`	List all accessible database schemas with default schema
`get-search-path`	Get current search_path and effective schema (read-only diagnostic)
`smart-query-builder`	Generate SQL from high-level intent
`optimize-query`	Run EXPLAIN, return plan, findings, and performance estimate
`validate-query`	Validate SQL syntax and flag risky patterns before execution
`analyze-data-lineage`	Trace FK-based upstream/downstream table dependencies
`discover-joins`	Discover foreign key relationships and suggest JOINs
`sample-data`	Fetch sample rows to infer data formats
`discover-insights`	Discover KPIs, trends, anomalies, and distribution patterns in database tables
`track-schema-changes`	Track schema snapshots/history, generate migrations, and detect schema drift
`federated-query`	Execute read-only cross-profile subqueries with optional JOINs, aggregation, and partial-failure metadata
`list-tools`	List all available MCP tools and descriptions
`get-tool-help`	Return on-demand summary, examples, and common errors for a tool
`analyze-schema`	Signal-based schema analysis with naming prefix frequencies, FK structural categorization, index coverage, and AI query suggestions
`mcp-info`	Show provider version and author

🤖 Model Compatibility

Default schema mode is compact for tool-first and strict declaration-budget clients.
Optional standard mode keeps verbose tool descriptions for human-readable metadata.
All 21 MCP tools are always registered.
Gemini Compatibility: Schemas are automatically sanitized to comply with Google Gemini's OpenAPI 3.0 subset requirements (single type values, no additionalProperties: false, proper items schemas).
Use get-tool-help for per-tool examples and troubleshooting without inflating startup metadata.

config.yaml:

schema_mode: compact # compact|standard

Recommended startup configuration for strict tool-loading clients:

schema_mode: compact

Helper tool request example:

{
  "tool_name": "execute-sql",
  "topic": "all"
}

📖 Documentation

Core Documentation

📋 API Documentation - Detailed API specifications and examples
📊 Implementation Status - Current implementation tracking
🏗️ Technical Specifications - Architecture and design details
📝 PRD Analysis - Product requirements analysis with AI perspective
🔍 Schema Introspection Queries - Database-specific queries
🧪 Test Enhanced Schema - Test schema documentation
🗺️ Enhancement Roadmap - Strategic development planning

Memory Bank Documentation

The project includes a comprehensive memory bank system for AI assistants, located in .kilocode/rules/memory-bank/:

🏗️ Architecture - System architecture and component relationships
📋 Brief - Project overview and requirements
📊 Context - Current state and recent changes
🎯 Product - Problem statement and solution overview
💻 Tech - Technology stack and development setup

Project Planning

🗺️ Roadmap - Consolidated enhancement plan
📊 Vertical Slices (History) - Phase-by-phase development breakdown
🔍 Architecture Validation (History) - Technical compatibility analysis
🐛 MCP Tool Detection Fix (History) - Critical bug fix documentation

Version History

📋 CHANGELOG - Detailed release notes and version history

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for development setup and workflow.

📄 License

This project is licensed under the MIT License.

🔐 Security

For security policies and vulnerability reporting, please see our Security Policy.

📜 Code of Conduct

Please read our Code of Conduct for community guidelines.

🧪 Testing

# Run all tests
go test ./...

# Run tests with coverage
go test -cover ./...

# Run specific test suites
go test ./internal/mcp -run "TestDescribe" -v  # MySQL/PostgreSQL descriptor tests
go test ./internal/mcp -run "TestLoadLineage" -v  # Lineage edge tests

Testing Strategy

MySQL/PostgreSQL: Uses go-sqlmock for database-specific query testing without requiring real database connections
SQLite: Uses real in-memory databases for actual SQLite functionality testing
Live Integration: Optional live database tests via DB_MCP_IT_* environment variables

📊 Project Status

Version: v1.6.3
Built with: Various vibe coding tools
Status: Production Ready ✅
All 21 MCP tools are fully implemented and OpenAPI-aligned.
Enhanced schema introspection and sample data features.
Optional advanced profiling in analyze-schema for column-level statistics, pattern detection, and quality scoring.
AES-GCM encryption, connection pooling, and structured error handling are enforced.
Comprehensive unit and integration tests included.
Ready for production use.
Business Intelligence Discovery: Added discover-insights tool for automatic KPI, trend, anomaly, and distribution analysis

Enhancement Planning

Current Development Status: The Database MCP Server is production-ready with a comprehensive enhancement roadmap in progress.

Implementation Phases:

Phase 1 (Completed): Query optimization, validation, and enhanced NLP
Phase 2 (Completed): Data lineage and business intelligence
Phase 3 (Completed): Schema evolution, advanced profiling, and multi-database federation
Phase 4 (Planned): Cross-database data migration with async jobs, schema translation, and resume capability

Current Progress:

track-schema-changes is implemented with snapshot tracking, history, migration generation, and drift detection.
Advanced profiling for analyze-schema is implemented with optional profiling parameter and backward-compatible response shape.
federated-query is implemented with parser/planner/join/executor/handler modules and dedicated test coverage.
configure-profile enhanced with delete and clone actions (v1.3.0).

Planning Documents:

Enhancement Roadmap - Strategic overview
Data Migration Design - Phase 4 implementation plan
Project Plan (History) - Comprehensive implementation strategy
Vertical Slices (History) - Detailed phase breakdowns

🔍 Multi-Schema Support (PostgreSQL)

This server provides comprehensive multi-schema support for PostgreSQL databases:

Schema-Aware Tools

Tool	Schema Support
`list-tables`	Returns schema information for all tables
`describe-table`	Optional `schema` parameter with auto-detection
`sample-data`	Optional `schema` parameter
`analyze-schema`	Optional `schema` parameter
`list-schemas`	Discover all accessible schemas
`get-search-path`	Diagnostic tool for current schema context

Connection Pooling Note

This server uses connection pooling per database profile. Due to this architecture:

set-search-path is intentionally NOT implemented - Session-level search_path changes would contaminate other clients using the same pooled connection
Use explicit schema qualification via the schema parameter on relevant tools
Auto-detection falls back gracefully: current_schema() → first accessible schema → 'public'

Example Usage

// Describe table in specific schema
{
  "profile_name": "mydb",
  "database_name": "mydb",
  "table_name": "users",
  "schema": "bitnami_redmine"
}

// List all schemas
{
  "profile_name": "mydb",
  "database_name": "mydb"
}

Ready for immediate enhancement development while maintaining production stability.

Database

guyinwonder1681stars

Production-ready MCP provider for SQL databases with unified AI agent interface, supporting MySQL, MariaDB, PostgreSQL, and SQLite with secure credential management and comprehensive query tools.

Databases

Install Run on PlugMCP

GitHub

Run on PlugMCP

1# Database

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-28

Stars: 1
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Database?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Database need an API key?

No required environment variable is listed on this record.

What transport does Database use?

Database runs locally over stdio.

When was Database last checked?

From the publisher

Documentation

Database MCP Server

🚀 Quick Start

# Clone the repository
git clone https://github.com/guyinwonder168/database-mcp-server.git
cd database-mcp-server

# Build the server
go build -o mcp-server ./cmd/server/main.go

# Run the server
./mcp-server

📦 Container Package (GHCR)

# Pull the release image
docker pull ghcr.io/guyinwonder168/database-mcp-server:v1.6.3

# Run with stdio transport
docker run --rm -i ghcr.io/guyinwonder168/database-mcp-server:v1.6.3

# Persist config.yaml and logs on host
mkdir -p ./.mcp-data
docker run --rm -i \
  -v "$(pwd)/.mcp-data:/app" \
  ghcr.io/guyinwonder168/database-mcp-server:v1.6.3

Package registry: https://github.com/guyinwonder168/database-mcp-server/pkgs/container/database-mcp-server

📋 Features

🔧 Interactive Setup - Auto-creates config.yaml if missing; all configuration is managed via MCP actions
👥 Profile Management - Create, update, delete, or clone database profiles via MCP
⚡ SQL Execution - Run arbitrary SQL queries (with read-only enforcement)
🔍 Schema Introspection - List tables/views with schema info, describe table schemas, list databases, discover joins, and browse schemas
🗂️ Multi-Schema Support - Work with tables in any PostgreSQL schema with automatic detection and schema-qualified queries
📊 Sample Data Fetching - Fetch sample rows to infer data formats and value ranges
🔗 Automated Join Discovery - Suggest JOIN SQL for building complex queries
🚦 Query Optimization - EXPLAIN-based analysis with findings and performance estimates
🛡️ Query Validation - Syntax, logic, and security checks before execution
🤖 Smart Query Builder - Generate SQL queries programmatically (integrated into analyze-schema AIQuerySuggestions)
🔒 Read-only Profiles - Prevent write operations on selected profiles
🔐 Secure Credentials - Passwords are encrypted at rest using AES-GCM (256-bit)
🏊 Connection Pooling - Efficient, configurable pooling with max pool size
📝 Structured Logging & Error Handling - All actions and errors are logged as structured JSON; actionable error responses
🛠️ Tool Discovery - list-tools MCP action returns a machine-readable list of all available tools/actions
🔌 Official MCP Protocol - Communication via stdio (not HTTP server; JSON is exchanged over stdio via official Go MCP SDK)
📈 Business Intelligence - Discover KPIs, trends, anomalies, and distribution patterns via discover-insights
🧭 Data Lineage - Analyze upstream/downstream dependencies via analyze-data-lineage
🧱 Schema Evolution Tracking - Track schema snapshots, detect drift, and generate migration scripts via track-schema-changes
🧬 Advanced Data Profiling - Optional statistical/pattern profiling for analyze-schema via profiling: true
🌐 Multi-Database Federation - Execute federated subqueries with cross-profile joins via federated-query

🛠️ Supported MCP Tools

Tool	Description
`configure-profile`	Create, update, delete, or clone database connection profiles
`list-profiles`	List all configured database profiles
`execute-sql`	Execute arbitrary SQL queries with read-only enforcement
`list-tables`	List tables with schema information in selected database
`describe-table`	Describe comprehensive table schema with metadata (supports schema parameter)
`list-databases`	List accessible databases for profile
`list-schemas`	List all accessible database schemas with default schema
`get-search-path`	Get current search_path and effective schema (read-only diagnostic)
`smart-query-builder`	Generate SQL from high-level intent
`optimize-query`	Run EXPLAIN, return plan, findings, and performance estimate
`validate-query`	Validate SQL syntax and flag risky patterns before execution
`analyze-data-lineage`	Trace FK-based upstream/downstream table dependencies
`discover-joins`	Discover foreign key relationships and suggest JOINs
`sample-data`	Fetch sample rows to infer data formats
`discover-insights`	Discover KPIs, trends, anomalies, and distribution patterns in database tables
`track-schema-changes`	Track schema snapshots/history, generate migrations, and detect schema drift
`federated-query`	Execute read-only cross-profile subqueries with optional JOINs, aggregation, and partial-failure metadata
`list-tools`	List all available MCP tools and descriptions
`get-tool-help`	Return on-demand summary, examples, and common errors for a tool
`analyze-schema`	Signal-based schema analysis with naming prefix frequencies, FK structural categorization, index coverage, and AI query suggestions
`mcp-info`	Show provider version and author

🤖 Model Compatibility

Default schema mode is compact for tool-first and strict declaration-budget clients.
Optional standard mode keeps verbose tool descriptions for human-readable metadata.
All 21 MCP tools are always registered.
Gemini Compatibility: Schemas are automatically sanitized to comply with Google Gemini's OpenAPI 3.0 subset requirements (single type values, no additionalProperties: false, proper items schemas).
Use get-tool-help for per-tool examples and troubleshooting without inflating startup metadata.

config.yaml:

schema_mode: compact # compact|standard

Recommended startup configuration for strict tool-loading clients:

schema_mode: compact

Helper tool request example:

{
  "tool_name": "execute-sql",
  "topic": "all"
}

📖 Documentation

Core Documentation

📋 API Documentation - Detailed API specifications and examples
📊 Implementation Status - Current implementation tracking
🏗️ Technical Specifications - Architecture and design details
📝 PRD Analysis - Product requirements analysis with AI perspective
🔍 Schema Introspection Queries - Database-specific queries
🧪 Test Enhanced Schema - Test schema documentation
🗺️ Enhancement Roadmap - Strategic development planning

Memory Bank Documentation

The project includes a comprehensive memory bank system for AI assistants, located in .kilocode/rules/memory-bank/:

🏗️ Architecture - System architecture and component relationships
📋 Brief - Project overview and requirements
📊 Context - Current state and recent changes
🎯 Product - Problem statement and solution overview
💻 Tech - Technology stack and development setup

Project Planning

🗺️ Roadmap - Consolidated enhancement plan
📊 Vertical Slices (History) - Phase-by-phase development breakdown
🔍 Architecture Validation (History) - Technical compatibility analysis
🐛 MCP Tool Detection Fix (History) - Critical bug fix documentation

Version History

📋 CHANGELOG - Detailed release notes and version history

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for development setup and workflow.

📄 License

This project is licensed under the MIT License.

🔐 Security

For security policies and vulnerability reporting, please see our Security Policy.

📜 Code of Conduct

Please read our Code of Conduct for community guidelines.

🧪 Testing

# Run all tests
go test ./...

# Run tests with coverage
go test -cover ./...

# Run specific test suites
go test ./internal/mcp -run "TestDescribe" -v  # MySQL/PostgreSQL descriptor tests
go test ./internal/mcp -run "TestLoadLineage" -v  # Lineage edge tests

Testing Strategy

MySQL/PostgreSQL: Uses go-sqlmock for database-specific query testing without requiring real database connections
SQLite: Uses real in-memory databases for actual SQLite functionality testing
Live Integration: Optional live database tests via DB_MCP_IT_* environment variables

📊 Project Status

Version: v1.6.3
Built with: Various vibe coding tools
Status: Production Ready ✅
All 21 MCP tools are fully implemented and OpenAPI-aligned.
Enhanced schema introspection and sample data features.
Optional advanced profiling in analyze-schema for column-level statistics, pattern detection, and quality scoring.
AES-GCM encryption, connection pooling, and structured error handling are enforced.
Comprehensive unit and integration tests included.
Ready for production use.
Business Intelligence Discovery: Added discover-insights tool for automatic KPI, trend, anomaly, and distribution analysis

Enhancement Planning

Current Development Status: The Database MCP Server is production-ready with a comprehensive enhancement roadmap in progress.

Implementation Phases:

Phase 1 (Completed): Query optimization, validation, and enhanced NLP
Phase 2 (Completed): Data lineage and business intelligence
Phase 3 (Completed): Schema evolution, advanced profiling, and multi-database federation
Phase 4 (Planned): Cross-database data migration with async jobs, schema translation, and resume capability

Current Progress:

track-schema-changes is implemented with snapshot tracking, history, migration generation, and drift detection.
Advanced profiling for analyze-schema is implemented with optional profiling parameter and backward-compatible response shape.
federated-query is implemented with parser/planner/join/executor/handler modules and dedicated test coverage.
configure-profile enhanced with delete and clone actions (v1.3.0).

Planning Documents:

Enhancement Roadmap - Strategic overview
Data Migration Design - Phase 4 implementation plan
Project Plan (History) - Comprehensive implementation strategy
Vertical Slices (History) - Detailed phase breakdowns

🔍 Multi-Schema Support (PostgreSQL)

This server provides comprehensive multi-schema support for PostgreSQL databases:

Schema-Aware Tools

Tool	Schema Support
`list-tables`	Returns schema information for all tables
`describe-table`	Optional `schema` parameter with auto-detection
`sample-data`	Optional `schema` parameter
`analyze-schema`	Optional `schema` parameter
`list-schemas`	Discover all accessible schemas
`get-search-path`	Diagnostic tool for current schema context

Connection Pooling Note

This server uses connection pooling per database profile. Due to this architecture:

set-search-path is intentionally NOT implemented - Session-level search_path changes would contaminate other clients using the same pooled connection
Use explicit schema qualification via the schema parameter on relevant tools
Auto-detection falls back gracefully: current_schema() → first accessible schema → 'public'

Example Usage

// Describe table in specific schema
{
  "profile_name": "mydb",
  "database_name": "mydb",
  "table_name": "users",
  "schema": "bitnami_redmine"
}

// List all schemas
{
  "profile_name": "mydb",
  "database_name": "mydb"
}

Ready for immediate enhancement development while maintaining production stability.