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@subnetmarco/pgmcp

MCP server for PostgreSQL natural language queries

PGMCP exposes an existing PostgreSQL database through the Model Context Protocol so agents can ask questions in plain English and get SQL-backed answers. It supports streaming, read-only query enforcement, free-text search, and multiple output formats. You point it at a database with `DATABASE_URL`, start the server, and connect it from MCP clients like Cursor or Claude Desktop. The server handles query generation, validation, and safer execution around the database.

539 starsβ€’62 forksβ€’Goβ€’Updated 3mo ago
Who it's for

Builders who want their agent to query an existing PostgreSQL database through MCP.

What it delivers

You can ask your database questions in plain English instead of writing SQL by hand.

What it does

Natural language to SQL

Turns plain-English questions into SQL queries against PostgreSQL.

Read-only query enforcement

Blocks write operations and runs queries in read-only transactions.

Automatic streaming

Streams large result sets with pagination instead of failing on big responses.

Text search across columns

Lets you search all text columns with the `search` tool.

Multiple output formats

Returns results as table, JSON, or CSV from the client.

MCP client support

Works with MCP-compatible clients such as Cursor and Claude Desktop.

How to get it

  1. 1Extract and run
    # Example for macOS/Linux
    tar xzf pgmcp_*.tar.gz
    cd pgmcp_*
    ./pgmcp-server
  2. 2Run
    # Homebrew (macOS/Linux) - Available after first release
    brew tap subnetmarco/homebrew-tap
    brew install pgmcp
    
    # Build from source
    go build -o pgmcp-server ./server
    go build -o pgmcp-client ./client
  3. 3Add -ldflags="-s -w -extldflags=-static" -trimpath if you want to get stripped…
    go build -ldflags="-s -w -extldflags=-static" -trimpath -o pgmcp-server ./server
    go build -ldflags="-s -w -extldflags=-static" -trimpath -o pgmcp-client ./client
  4. 4Run
    # Docker
    docker run -e DATABASE_URL="postgres://user:pass@host:5432/db" \
      -p 8080:8080 ghcr.io/subnetmarco/pgmcp:latest
    
    # Kubernetes (see examples/ directory for full manifests)
    kubectl create secret generic pgmcp-secret \
      --from-literal=database-url="postgres://user:pass@host:5432/db"
    kubectl apply -f examples/k8s/

README

ci Go Report Card License

PGMCP - PostgreSQL Model Context Protocol Server

PGMCP connects AI assistants to any PostgreSQL database through natural language queries. Ask questions in plain English and get structured SQL results with automatic streaming and robust error handling.

Works with: Cursor, Claude Desktop, VS Code extensions, and any MCP-compatible client

Quick Start

PGMCP connects to your existing PostgreSQL database and makes it accessible to AI assistants through natural language queries.

Prerequisites

  • PostgreSQL database (existing database with your schema)
  • OpenAI API key (optional, for AI-powered SQL generation)

Basic Usage

# Set up environment variables
export DATABASE_URL="postgres://user:password@localhost:5432/your-existing-db"
export OPENAI_API_KEY="your-api-key"  # Optional

# Run server (using pre-compiled binary)
./pgmcp-server

# Test with client in another terminal
./pgmcp-client -ask "What tables do I have?" -format table
./pgmcp-client -ask "Who is the customer that has placed the most orders?" -format table
./pgmcp-client -search "john" -format table

Here is how it works:

πŸ‘€ User / AI Assistant
         β”‚
         β”‚ "Who are the top customers?"
         β–Ό
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚                    Any MCP Client                           β”‚
β”‚                                                             β”‚
β”‚  PGMCP CLI  β”‚  Cursor  β”‚  Claude Desktop  β”‚  VS Code  β”‚ ... β”‚
β”‚  JSON/CSV   β”‚  Chat    β”‚  AI Assistant    β”‚  Editor   β”‚     β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
         β”‚
         β”‚ Streamable HTTP / MCP Protocol
         β–Ό
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚                    PGMCP Server                             β”‚
β”‚                                                             β”‚
β”‚  πŸ”’ Security    🧠 AI Engine      🌊 Streaming              β”‚
β”‚  β€’ Input Valid  β€’ Schema Cache    β€’ Auto-Pagination         β”‚
β”‚  β€’ Audit Log    β€’ OpenAI API      β€’ Memory Management       β”‚
β”‚  β€’ SQL Guard    β€’ Error Recovery  β€’ Connection Pool         β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
         β”‚
         β”‚ Read-Only SQL Queries
         β–Ό
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚                Your PostgreSQL Database                     β”‚
β”‚                                                             β”‚
β”‚  Any Schema: E-commerce, Analytics, CRM, etc.               β”‚
β”‚  Tables β€’ Views β€’ Indexes β€’ Functions                       β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

External AI Services:
OpenAI API β€’ Anthropic β€’ Local LLMs (Ollama, etc.)

Key Benefits:
βœ… Works with ANY PostgreSQL database (no assumptions about schema)
βœ… No schema modifications required  
βœ… Read-only access (100% safe)
βœ… Automatic streaming for large results
βœ… Intelligent query understanding (singular vs plural)
βœ… Robust error handling (graceful AI failure recovery)
βœ… PostgreSQL case sensitivity support (mixed-case tables)
βœ… Production-ready security and performance
βœ… Universal database compatibility
βœ… Multiple output formats (table, JSON, CSV)
βœ… Free-text search across all columns
βœ… Authentication support
βœ… Comprehensive testing suite

Features

  • Natural Language to SQL: Ask questions in plain English
  • Automatic Streaming: Handles large result sets automatically
  • Safe Read-Only Access: Prevents any write operations
  • Text Search: Search across all text columns
  • Multiple Output Formats: Table, JSON, and CSV
  • PostgreSQL Case Sensitivity: Handles mixed-case table names correctly
  • Universal Compatibility: Works with any PostgreSQL database

Environment Variables

Required:

  • DATABASE_URL: PostgreSQL connection string to your existing database

Optional:

  • OPENAI_API_KEY: OpenAI API key for AI-powered SQL generation
  • OPENAI_MODEL: Model to use (default: "gpt-4o-mini")
  • HTTP_ADDR: Server address (default: ":8080")
  • HTTP_PATH: MCP endpoint path (default: "/mcp")
  • AUTH_BEARER: Bearer token for authentication

Installation

Download Pre-compiled Binaries

  1. Go to GitHub Releases
  2. Download the binary for your platform (Linux, macOS, Windows)
  3. Extract and run:
# Example for macOS/Linux
tar xzf pgmcp_*.tar.gz
cd pgmcp_*
./pgmcp-server

Alternative Options

# Homebrew (macOS/Linux) - Available after first release
brew tap subnetmarco/homebrew-tap
brew install pgmcp

# Build from source
go build -o pgmcp-server ./server
go build -o pgmcp-client ./client

Add -ldflags="-s -w -extldflags=-static" -trimpath if you want to get stripped executables (no debug info):

go build -ldflags="-s -w -extldflags=-static" -trimpath -o pgmcp-server ./server
go build -ldflags="-s -w -extldflags=-static" -trimpath -o pgmcp-client ./client

Docker/Kubernetes

# Docker
docker run -e DATABASE_URL="postgres://user:pass@host:5432/db" \
  -p 8080:8080 ghcr.io/subnetmarco/pgmcp:latest

# Kubernetes (see examples/ directory for full manifests)
kubectl create secret generic pgmcp-secret \
  --from-literal=database-url="postgres://user:pass@host:5432/db"
kubectl apply -f examples/k8s/

Quick Start

# Set up database (optional - works with any existing PostgreSQL database)
export DATABASE_URL="postgres://user:password@localhost:5432/mydb"
psql $DATABASE_URL < schema.sql

# Run server
export OPENAI_API_KEY="your-api-key"
./pgmcp-server

# Test with client
./pgmcp-client -ask "Who is the user that places the most orders?" -format table
./pgmcp-client -ask "Show me the top 40 most reviewed items in the marketplace" -format table

Environment Variables

Required:

  • DATABASE_URL: PostgreSQL connection string

Optional:

  • OPENAI_API_KEY: OpenAI API key for SQL generation
  • OPENAI_MODEL: Model to use (default: "gpt-4o-mini")
  • HTTP_ADDR: Server address (default: ":8080")
  • HTTP_PATH: MCP endpoint path (default: "/mcp")
  • AUTH_BEARER: Bearer token for authentication

Usage Examples

# Ask questions in natural language
./pgmcp-client -ask "What are the top 5 customers?" -format table
./pgmcp-client -ask "How many orders were placed today?" -format json

# Search across all text fields
./pgmcp-client -search "john" -format table

# Multiple questions at once
./pgmcp-client -ask "Show tables" -ask "Count users" -format table

# Different output formats
./pgmcp-client -ask "Export all data" -format csv -max-rows 1000

Example Database

The project includes two schemas:

  • schema.sql: Full Amazon-like marketplace with 5,000+ records
  • schema_minimal.sql: Minimal test schema with mixed-case "Categories" table

Key features:

  • Mixed-case table names ("Categories") for testing case sensitivity
  • Composite primary keys (order_items) for testing AI assumptions
  • Realistic relationships and data types

Use your own database:

export DATABASE_URL="postgres://user:pass@host:5432/your_db"
./pgmcp-server
./pgmcp-client -ask "What tables do I have?"

AI Error Handling

When AI generates incorrect SQL, PGMCP handles it gracefully:

{
  "error": "Column not found in generated query",
  "suggestion": "Try rephrasing your question or ask about specific tables",
  "original_sql": "SELECT non_existent_column FROM table..."
}

Instead of crashing, the system provides helpful feedback and continues operating.

MCP Integration

Cursor Integration

# Start server
export DATABASE_URL="postgres://user:pass@localhost:5432/your_db"
./pgmcp-server

Add to Cursor settings:

{
  "mcp.servers": {
    "pgmcp": {
      "transport": {
        "type": "http",
        "url": "http://localhost:8080/mcp"
      }
    }
  }
}

Claude Desktop Integration

Edit ~/.config/claude-desktop/claude_desktop_config.json:

{
  "mcpServers": {
    "pgmcp": {
      "transport": {
        "type": "http",
        "url": "http://localhost:8080/mcp"
      }
    }
  }
}

API Tools

  • ask: Natural language questions β†’ SQL queries with automatic streaming
  • search: Free-text search across all database text columns
  • stream: Advanced streaming for very large result sets with pagination

Safety Features

  • Read-Only Enforcement: Blocks write operations (INSERT, UPDATE, DELETE, etc.)
  • Query Timeouts: Prevents long-running queries
  • Input Validation: Sanitizes and validates all user input
  • Transaction Isolation: All queries run in read-only transactions

Testing

# Unit tests
go test ./server -v

# Integration tests (requires PostgreSQL)
go test ./server -tags=integration -v

License

Apache 2.0 - See LICENSE file for details.

Related Projects


PGMCP makes your PostgreSQL database accessible to AI assistants through natural language while maintaining security through read-only access controls.

Files in the repo

Repository payloadβ€’15 top-level entries
  • .github
  • client
  • examples
  • scripts
  • server
  • .gitignore
  • .goreleaser.yaml
  • Dockerfile
  • go.mod
  • go.sum
  • LICENSE
  • README.md
  • RELEASING.md
  • schema_minimal.sql
  • schema.sql

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