Supatool

Schema Management CLI for PostgreSQL. Works with Cloud SQL, Supabase, and any PostgreSQL database. Extract schemas into LLM-friendly structures, deploy diffs, apply migrations, and export seeds.

Why Supatool?

Modern AI coding tools (Cursor, Claude, MCP) often struggle with large database schemas. Typical issues include:

• Token Waste: Reading the entire schema at once consumes 10k+ tokens.
• Lost Context: Frequent API calls to fetch table details lead to fragmented reasoning.
• Inaccuracy: AI misses RLS policies or complex FK relations split across multiple files.

Supatool solves this by reorganizing your schema into a highly searchable, indexed, and modular structure that helps AI "understand" your DB with minimal tokens.

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Supported Databases

Any PostgreSQL database:

• Google Cloud SQL (PostgreSQL)
• Supabase
• Amazon RDS (PostgreSQL)
• Self-hosted PostgreSQL

Connection strings in both postgresql:// and postgres:// formats are accepted.

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Key Features

• Extract (AI-Optimized) – DDL, RLS, and Triggers are bundled into one file per table. AI gets the full picture of a table by opening just one file.
• llms.txt Catalog – Automatically generates a standard llms.txt listing all OBJECTS, RELATIONS (FKs), and RPC dependencies. This serves as the "Map" for AI agents.
• Multi-Schema Support – Group objects by schema (e.g., public, agent, auth) with proper schema-qualification in SQL.
• Migrate – Apply pending db/migrations/*.sql files to remote, with tracking and transaction safety.
• Seed for AI – Export table data as JSON. Includes a dedicated llms.txt for seeds so AI can see real data structures.
• Safe Deploy – Push local schema changes with --dry-run to preview DDL before execution.

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Quick Start

npm install -g supatool

# Set connection string in .env.local
echo 'DB_CONNECTION_STRING=postgresql://user:password@host:5432/dbname' > .env.local

# Generate config
supatool config:init

# Extract schema and generate AI-ready docs
supatool extract --all -o db/schemas

Output Structure

db/schemas/
├── llms.txt          # 🗺️ THE ENTRY POINT: Read this first to understand the DB map
├── schema_index.json # 🤖 For JSON-parsing agents
├── schema_summary.md # 📄 Single-file overview for quick human/AI scanning
├── README.md         # Navigation guide
└── [schema_name]/
    ├── tables/       # table_name.sql (DDL + RLS + Triggers)
    ├── views/
    └── rpc/

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Best Practices for AI Agents (Cursor / Claude / MCP)

1. Start with the Map: Always ask the AI to read db/schemas/llms.txt first.
2. Targeted Reading: Once the AI identifies the relevant tables, instruct it to open only those specific .sql files.
3. Understand Relations: Use the RELATIONS section in llms.txt to help the AI write accurate JOINs.
4. RPC Context: If using functions, refer to RPC_TABLES in llms.txt to know which tables are affected.

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Commands

Extract

Pull schema from remote DB into local files:

supatool extract --all -o db/schemas
# Options:
# --schema public,agent   Specify schemas
# -t "user_*"             Filter tables by pattern
# --force                 Clear output dir before writing

Deploy

Push local schema changes to remote (diff → migration → apply):

supatool deploy --table users --dry-run   # preview
supatool deploy --table all --dry-run     # all tables
supatool deploy --table users             # confirm before apply

Migrate

Apply pending SQL files from db/migrations/ to remote:

supatool migrate                    # apply pending migrations
supatool migrate --dry-run          # preview only
supatool migrate -d path/to/dir     # custom directory

Migration files are applied in alphabetical order. Applied files are tracked in a _supatool_migrations table (auto-created).

Seed

Export table data as JSON for AI reference or testing:

supatool seed --tables tables.yaml

# tables.yaml format:
# public:
#   - users
#   - posts

Outputs JSON files and a llms.txt index in db/seeds/.

Config

supatool config:init    # generate supatool.config.json + .env.local template

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Configuration

supatool.config.json:

{
  "schemaDir": "./db/schemas",
  "tablePattern": "*",
  "migration": {
    "naming": "timestamp",
    "dir": "db/migrations"
  }
}

.env.local (never commit):

DB_CONNECTION_STRING=postgresql://user:password@host:5432/dbname

Legacy env vars are also accepted: SUPABASE_CONNECTION_STRING, DATABASE_URL.

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Repository

GitHub · npm

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Works with any PostgreSQL database. Always backup your DB before deployment.