by antonorlov
Connect and manage your PostgreSQL database with support for SQL queries, table management, and schema inspection, inclu
Connects to PostgreSQL databases to execute SQL queries, manage tables, and inspect database schemas. Supports prepared statements with multiple parameter styles.
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.
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.
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.
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
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
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Share your MCP server with the developer community
We evaluated PostgreSQL against two servers with overlapping tools; this profile had the clearer scope statement.
I recommend PostgreSQL for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
PostgreSQL is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
According to our notes, PostgreSQL benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
PostgreSQL has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
We wired PostgreSQL into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
PostgreSQL is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We evaluated PostgreSQL against two servers with overlapping tools; this profile had the clearer scope statement.
PostgreSQL reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Useful MCP listing: PostgreSQL is the kind of server we cite when onboarding engineers to host + tool permissions.
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A Model Context Protocol server that provides PostgreSQL database operations. This server enables AI models to interact with PostgreSQL databases through a standardized interface.
npm install mcp-postgres-server
Or run directly with:
npx mcp-postgres-server
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"
}
}
}
}
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"
}
});
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]
}
});
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]"]
}
});
List all schemas in the connected database.
use_mcp_tool({
server_name: "postgres",
tool_name: "list_schemas",
arguments: {}
});
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"
}
});
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"
}
});
The server provides detailed error messages for common issues:
MIT
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
Prerequisites
Time Estimate
15-30 minutes including configuration and testing
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
Compatibility
✓ 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.