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Documentation Index

Fetch the complete documentation index at: https://docs.anysite.io/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

Before loading or querying data, it helps to understand what is already in your database. The discovery commands introspect any connected SQLite, PostgreSQL, or ClickHouse database and build a detailed structural catalog — tables, columns, types, indexes, foreign keys, row counts, and sample data. Optionally enrich the catalog with LLM-generated descriptions for instant context. Discovered catalogs are saved locally and can be browsed anytime — useful for AI agents that need database context without manual documentation.

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Discover Database Structure

Run discovery against any configured connection: 
anysite db discover mydb
This introspects the database and outputs:
  • Tables with row counts
  • Columns — name, type, nullability, defaults, primary keys
  • Indexes — name, columns, uniqueness
  • Foreign keys — source and target columns
  • Sample data — first rows from each table
  • Read-only status — auto-detected (PostgreSQL replicas, read-only filesystems, ClickHouse readonly setting)
The result is automatically saved as a catalog for future reference.
Discovery works with SQLite (via PRAGMAs), PostgreSQL (via information_schema and pg_catalog), and ClickHouse (via system.tables and system.columns). Note: ClickHouse does not support foreign keys, so FK discovery returns empty results.

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LLM Enrichment

Add human-readable descriptions to your catalog using an LLM: 
anysite db discover mydb --with-llm
Requires the LLM extra: pip install "anysite-cli[llm]" and a configured LLM provider (anysite llm setup).
LLM enrichment adds four layers of context:
LayerWhat It Generates
Table descriptionsPurpose and role of each table
Column descriptionsSemantic meaning of each column
Implicit relationshipsNaming-pattern detection (e.g., user_id → users.id) beyond declared FKs
Database descriptionOverall summary of the database structure and purpose
This context is saved in the catalog and can be injected into LLM prompts via to_context_string().

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Filtering Tables

Control which tables to discover: 
# Discover specific tables only
anysite db discover mydb --tables users,posts,comments

# Exclude internal tables
anysite db discover mydb --exclude-tables _migrations,django_session

# Control sample data rows (default: 3)
anysite db discover mydb --sample-rows 10

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Discovery Options

OptionDescriptionDefault
--tablesComma-separated list of tables to includeAll tables
--exclude-tablesComma-separated list of tables to skipNone
--sample-rowsNumber of sample rows per table3
--with-llmEnrich with LLM-generated descriptionsOff

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Browse Saved Catalogs

After discovery, catalogs are saved at ~/.anysite/catalogs/<connection>.yaml and can be browsed anytime: 
# List all saved catalogs
anysite db catalog

# View a specific catalog
anysite db catalog mydb

# View a single table from the catalog
anysite db catalog mydb --table users

# JSON output (for agents and scripts)
anysite db catalog mydb --json
Use anysite db catalog mydb --json to pipe database context into AI agents. The JSON format includes all tables, columns, relationships, and LLM descriptions — everything an agent needs to understand your data.

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Commands Reference

CommandDescription
anysite db discover <conn>Discover and save database structure
anysite db discover <conn> --with-llmDiscover with LLM-generated descriptions
anysite db discover <conn> --tables t1,t2Discover specific tables only
anysite db discover <conn> --exclude-tables t1Exclude tables from discovery
anysite db discover <conn> --sample-rows NControl sample data rows
anysite db catalogList all saved catalogs
anysite db catalog <conn>View saved catalog for a connection
anysite db catalog <conn> --table <name>View a specific table from catalog
anysite db catalog <conn> --jsonOutput catalog as JSON

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Next Steps

Database Operations

Insert, query, and sync data with your databases

Agent Protocol

Structured JSON output for AI agent integration