PostgreSQL▌
by antonorlov
Connect and manage your PostgreSQL database with support for SQL queries, table management, and schema inspection, inclu
Provides a bridge to PostgreSQL databases for executing SQL queries, managing tables, and inspecting schemas with support for prepared statements and multiple parameter styles
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Developers building database-driven applications
- / Data analysts querying PostgreSQL databases
- / Database administrators managing schemas
- / Applications needing secure parameterized queries
capabilities
- / Execute SELECT queries with parameters
- / Run INSERT, UPDATE, DELETE operations
- / List database schemas and tables
- / Inspect table structures and columns
- / Use prepared statements for security
- / Connect to remote PostgreSQL instances
what it does
Connects to PostgreSQL databases to execute SQL queries, manage tables, and inspect database schemas. Supports prepared statements with multiple parameter styles.
about
PostgreSQL is a community-built MCP server published by antonorlov that provides AI assistants with tools and capabilities via the Model Context Protocol. Connect and manage your PostgreSQL database with support for SQL queries, table management, and schema inspection, inclu It is categorized under databases.
how to install
You can install PostgreSQL in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
PostgreSQL is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
MCP PostgreSQL Server
A Model Context Protocol server that provides PostgreSQL database operations. This server enables AI models to interact with PostgreSQL databases through a standardized interface.
Installation
Manual Installation
npm install mcp-postgres-server
Or run directly with:
npx mcp-postgres-server
Configuration
The server requires the following environment variables:
{
"mcpServers": {
"postgres": {
"type": "stdio",
"command": "npx",
"args": ["-y", "mcp-postgres-server"],
"env": {
"PG_HOST": "your_host",
"PG_PORT": "5432",
"PG_USER": "your_user",
"PG_PASSWORD": "your_password",
"PG_DATABASE": "your_database"
}
}
}
}
Available Tools
1. connect_db
Establish connection to PostgreSQL database using provided credentials.
use_mcp_tool({
server_name: "postgres",
tool_name: "connect_db",
arguments: {
host: "localhost",
port: 5432,
user: "your_user",
password: "your_password",
database: "your_database"
}
});
2. query
Execute SELECT queries with optional prepared statement parameters. Supports both PostgreSQL-style ($1, $2) and MySQL-style (?) parameter placeholders.
use_mcp_tool({
server_name: "postgres",
tool_name: "query",
arguments: {
sql: "SELECT * FROM users WHERE id = $1",
params: [1]
}
});
3. execute
Execute INSERT, UPDATE, or DELETE queries with optional prepared statement parameters. Supports both PostgreSQL-style ($1, $2) and MySQL-style (?) parameter placeholders.
use_mcp_tool({
server_name: "postgres",
tool_name: "execute",
arguments: {
sql: "INSERT INTO users (name, email) VALUES ($1, $2)",
params: ["John Doe", "[email protected]"]
}
});
4. list_schemas
List all schemas in the connected database.
use_mcp_tool({
server_name: "postgres",
tool_name: "list_schemas",
arguments: {}
});
5. list_tables
List tables in the connected database. Accepts an optional schema parameter (defaults to 'public').
// List tables in the 'public' schema (default)
use_mcp_tool({
server_name: "postgres",
tool_name: "list_tables",
arguments: {}
});
// List tables in a specific schema
use_mcp_tool({
server_name: "postgres",
tool_name: "list_tables",
arguments: {
schema: "my_schema"
}
});
6. describe_table
Get the structure of a specific table. Accepts an optional schema parameter (defaults to 'public').
// Describe a table in the 'public' schema (default)
use_mcp_tool({
server_name: "postgres",
tool_name: "describe_table",
arguments: {
table: "users"
}
});
// Describe a table in a specific schema
use_mcp_tool({
server_name: "postgres",
tool_name: "describe_table",
arguments: {
table: "users",
schema: "my_schema"
}
});
Features
- Secure connection handling with automatic cleanup
- Prepared statement support for query parameters
- Support for both PostgreSQL-style ($1, $2) and MySQL-style (?) parameter placeholders
- Comprehensive error handling and validation
- TypeScript support
- Automatic connection management
- Supports PostgreSQL-specific syntax and features
- Multi-schema support for database operations
Security
- Uses prepared statements to prevent SQL injection
- Supports secure password handling through environment variables
- Validates queries before execution
- Automatically closes connections when done
Error Handling
The server provides detailed error messages for common issues:
- Connection failures
- Invalid queries
- Missing parameters
- Database errors
License
MIT
FAQ
- What is the PostgreSQL MCP server?
- PostgreSQL is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for PostgreSQL?
- This profile displays 60 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.4 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Direct Database Queries from AI
Enable Claude to query your database directly using natural language
Example
Ask 'Show me top 10 customers by revenue this month' and get SQL results instantly
Eliminate manual SQL writing for ad-hoc queries, get insights 10x faster
Data Analysis & Reporting
Generate complex reports and analytics without leaving conversation
Example
Analyze sales trends, cohort retention, user behavior patterns conversationally
Democratize data access—non-technical team members can query databases
Schema Exploration
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Data Validation & Quality Checks
Run data quality queries to catch anomalies and inconsistencies
Example
Find duplicate records, missing values, orphaned foreign keys automatically
Maintain data integrity with less manual SQL work
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor with MCP support
- ›Database credentials (read-only recommended for safety)
- ›Network access from Claude client to database
- ›Understanding of database security and access control
Time Estimate
15-30 minutes including configuration and testing
Installation Steps
- 1.Install MCP server: npm install -g @modelcontextprotocol/server-[name]
- 2.Configure database connection in Claude Desktop config (~/.claude/mcp.json)
- 3.Provide connection string: host, port, database, username, password
- 4.Restart Claude Desktop to load MCP server
- 5.Test connection: 'List all tables in database'
- 6.Run simple query: 'Show me 5 rows from users table'
- 7.Verify results and permissions are correct
- 8.Document query patterns for team use
Troubleshooting
- ⚠Connection refused: Check database is running and network accessible
- ⚠Authentication failed: Verify credentials, check user permissions
- ⚠Claude can't see tables: Grant appropriate read permissions to database user
- ⚠Slow queries: Add indexes, limit result set size, use read replicas
- ⚠MCP server not loading: Check config syntax, restart Claude Desktop
Best Practices▌
✓ Do
- +Use read-only database credentials to prevent accidental writes
- +Connect to read replica, not production primary database
- +Set query timeout limits to prevent long-running queries
- +Document database schema and common queries for AI context
- +Monitor query performance and optimize slow queries
- +Use connection pooling for better performance
- +Test with non-production data first
✗ Don't
- −Don't use production write credentials—risk of data corruption
- −Don't query production database during peak traffic hours
- −Don't expose sensitive PII without proper access controls
- −Don't skip query result validation—AI can misinterpret schema
- −Don't allow unlimited result set sizes—set LIMIT clauses
- −Don't share database credentials in plain text config files
💡 Pro Tips
- ★Create database views for common queries to simplify AI access
- ★Add schema comments/descriptions so AI understands column meanings
- ★Use semantic table/column names ('customer_lifetime_value' not 'clv')
- ★Set up query logging to audit what Claude is querying
- ★Create saved query templates for recurring analysis
- ★Combine with data visualization tools for better insights
Technical Details▌
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
- Model Context Protocol (MCP)
- Database-specific protocols (PostgreSQL, MySQL, MongoDB)
Compatibility
- PostgreSQL
- MySQL
- SQLite
- MongoDB
- Redis
When to Use This▌
✓ Use When
Use for ad-hoc data queries, exploratory analysis, report generation, schema exploration, and democratizing data access. Best for read-heavy analytics workloads.
✗ Avoid When
Avoid for production write operations, mission-critical transactions, real-time OLTP workloads, or when database contains sensitive PII without proper access controls. Use read replicas, not primary.
Integration▌
- →Read replica connection for analytics queries
- →Database view layer to abstract complex joins
- →Query result caching for repeated questions
- →Audit logging of all AI-generated queries
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.4★★★★★60 reviews- ★★★★★Harper Gupta· Dec 24, 2024
We evaluated PostgreSQL against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Valentina Robinson· Dec 16, 2024
I recommend PostgreSQL for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Li Dixit· Dec 12, 2024
PostgreSQL is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Carlos Khan· Dec 8, 2024
According to our notes, PostgreSQL benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Carlos Garcia· Dec 8, 2024
PostgreSQL has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Harper Lopez· Nov 27, 2024
We wired PostgreSQL into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Harper Ndlovu· Nov 27, 2024
PostgreSQL is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Harper Bansal· Nov 19, 2024
We evaluated PostgreSQL against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Sakura Menon· Nov 7, 2024
PostgreSQL reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Sophia Malhotra· Oct 26, 2024
Useful MCP listing: PostgreSQL is the kind of server we cite when onboarding engineers to host + tool permissions.
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