sqlsaber 0.11.0

SQLSaber - Agentic SQL assistant like Claude Code

SQLSaber

Use the agent Luke!

SQLSaber is an agentic SQL assistant. Think Claude Code but for SQL.

Ask your questions in natural language and it will gather the right context automatically and answer your query by writing SQL and analyzing the results.

Table of Contents

• Features
• Installation
• Configuration
  • Database Connection
  • AI Model Configuration
  • Memory Management
• Usage
  • Interactive Mode
  • Single Query
  • Database Selection
• Examples
• MCP Server Integration
  • Starting the MCP Server
  • Configuring MCP Clients
  • Available MCP Tools
• How It Works
• Contributing
• License

Features

• Natural language to SQL conversion
• 🔍 Automatic database schema introspection
• 🛡️ Safe query execution (read-only by default)
• 🧠 Memory management
• 💬 Interactive REPL mode
• 🎨 Beautiful formatted output with syntax highlighting
• 🗄️ Support for PostgreSQL, SQLite, and MySQL
• 🔌 MCP (Model Context Protocol) server support

Installation

uv

uv tool install sqlsaber

pipx

pipx install sqlsaber

brew

brew install uv
uv tool install sqlsaber

Configuration

Database Connection

Set your database connection URL:

saber db add DB_NAME

This will ask you some questions about your database connection

AI Model Configuration

SQLSaber uses Sonnet-4 by default. You can change it using:

saber models set

# for more model settings run:
saber models --help

Memory Management

You can add specific context about your database to the model using the memory feature. This is similar to how you add memory/context in Claude Code.

saber memory add 'always convert dates to string for easier formating'

View all memories

saber memory list

You can also add memories in an interactive query session by starting with the # sign

Usage

Interactive Mode

Start an interactive session:

saber

You can also add memories in an interactive session by starting your message with the # sign

Single Query

Execute a single natural language query:

saber "show me all users created this month"

You can also pipe queries from stdin:

echo "show me all users created this month" | saber
cat query.txt | saber

Database Selection

Use a specific database connection:

# Interactive mode with specific database
saber -d mydb

# Single query with specific database
saber -d mydb "count all orders"

Examples

# Show database schema
saber "what tables are in my database?"

# Count records
saber "how many active users do we have?"

# Complex queries with joins
saber "show me orders with customer details for this week"

# Aggregations
saber "what's the total revenue by product category?"

# Date filtering
saber "list users who haven't logged in for 30 days"

# Data exploration
saber "show me the distribution of customer ages"

# Business analytics
saber "which products had the highest sales growth last quarter?"

# Start interactive mode
saber

MCP Server Integration

SQLSaber includes an MCP (Model Context Protocol) server that allows AI agents like Claude Code to directly leverage tools available in SQLSaber.

Starting the MCP Server

Run the MCP server using uvx:

uvx --from sqlsaber saber-mcp

Configuring MCP Clients

Claude Code

Add SQLSaber as an MCP server in Claude Code:

claude mcp add -- uvx --from sqlsaber saber-mcp

Other MCP Clients

For other MCP clients, configure them to run the command: uvx --from sqlsaber saber-mcp

Available MCP Tools

Once connected, the MCP client will have access to these tools:

• get_databases() - Lists all configured databases
• list_tables(database) - Get all tables in a database with row counts
• introspect_schema(database, table_pattern?) - Get detailed schema information
• execute_sql(database, query, limit?) - Execute SQL queries (read-only)

The MCP server uses your existing SQLSaber database configurations, so make sure to set up your databases using saber db add first.

How It Works

SQLSaber uses a multi-step process to gather the right context, provide it to the model, and execute SQL queries to get the right answers:

🔍 Discovery Phase

1. List Tables Tool: Quickly discovers available tables with row counts
2. Pattern Matching: Identifies relevant tables based on your query

📋 Schema Analysis

3. Smart Schema Introspection: Analyzes only the specific table structures needed for your query

⚡ Execution Phase

4. SQL Generation: Creates optimized SQL queries based on natural language input
5. Safe Execution: Runs read-only queries with built-in protections against destructive operations
6. Result Formatting: Presents results with explanations in tables and optionally, visualizes using plots

Contributing

Contributions are welcome! Please feel free to open an issue to discuss your ideas or report bugs.

License

This project is licensed under Apache-2.0 License - see the LICENSE file for details.