PostgreSQL Model Context Protocol (PG-MCP) Server
About
# PostgreSQL Model Context Protocol (PG-MCP) Server A Model Context Protocol (MCP) server for PostgreSQL databases with enhanced capabilities for AI agents. More info on the pg-mcp project here: ### [https://stuzero.github.io/pg-mcp/](https://stuzero.github.io/pg-mcp/) ## Overview PG-MCP is a server implementation of…
Explore
- Connect tool to register PostgreSQL connection strings and get a secure connection ID
- Read-only SQL execution (pg_query) with connection ID
- pg_explain tool to analyze query execution plans in JSON
- Schema discovery: list schemas, tables, columns, constraints, indexes, extensions
- Sample table data with pagination and approximate row counts
- Built-in YAML-based context for extensions like PostGIS and pgvector
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
PostgreSQL Model Context Protocol (PG-MCP) ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
- Python 3.13+
- PostgreSQL database(s)
```bash
uv sync
source .venv/bin/activate # On Windows: .venv\Scripts\activate
- pg_query: Execute read-only SQL queries using a connection ID
- pg_explain: Analyze query execution plans in JSON format
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"postgresql model context protocol (pg-mcp) server": {
"pg-mcp-server": {
"command": "uv",
"args": [
"sync"
]
}
}
}
}McpServers
{
"pg-mcp-server": {
"command": "uv",
"args": [
"sync"
]
}
}A Model Context Protocol (MCP) server for PostgreSQL databases with enhanced capabilities for AI agents.
More info on the pg-mcp project here:
https://stuzero.github.io/pg-mcp/
Overview
PG-MCP is a server implementation of the Model Context Protocol for PostgreSQL databases. It provides a comprehensive API for AI agents to discover, connect to, query, and understand PostgreSQL databases through MCP's resource-oriented architecture.
This implementation builds upon and extends the reference Postgres MCP implementation with several key enhancements:
1. Full Server Implementation: Built as a complete server with SSE transport for production use
2. Multi-database Support: Connect to multiple PostgreSQL databases simultaneously
3. Rich Catalog Information: Extracts and exposes table/column descriptions from the database catalog
4. Extension Context: Provides detailed YAML-based knowledge about PostgreSQL extensions like PostGIS and pgvector
5. Query Explanation: Includes a dedicated tool for analyzing query execution plans
6. Robust Connection Management: Proper lifecycle for database connections with secure connection ID handling
Features
Connection Management
- Connect Tool: Register PostgreSQL connection strings and get a secure connection ID
- Disconnect Tool: Explicitly close database connections when done
- Connection Pooling: Efficient connection management with pooling
Query Tools
- pg_query: Execute read-only SQL queries using a connection ID
- pg_explain: Analyze query execution plans in JSON format
Schema Discovery Resources
- List schemas with descriptions
- List tables with descriptions and row counts
- Get column details with data types and descriptions
- View table constraints and indexes
- Explore database extensions
Data Access Resources
- Sample table data (with pagination)
- Get approximate row counts
Extension Context
Built-in contextual information for PostgreSQL extensions like:
- PostGIS: Spatial data types, functions, and examples
- pgvector: Vector similarity search functions and best practices
Additional extensions can be easily added via YAML config files.
Installation
Prerequisites
- Python 3.13+
- PostgreSQL database(s)
Using Docker
```bash
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