Jump to content

Connect SuperML | Leeroopedia MCP: Equip your AI agents with best practices, code verification, and debugging knowledge. Powered by Leeroo — building Organizational Superintelligence. Contact us at founders@leeroo.com.

Principle:PrefectHQ Prefect Settings Discovery And Validation

From Leeroopedia


Knowledge Sources
Domains Configuration, Settings, Validation
Last Updated 2026-02-09 22:00 GMT

Overview

Principle of providing a machine-readable schema for all application settings that maps each setting to its environment variable, type, and default value.

Description

Settings Discovery and Validation is the practice of generating a comprehensive JSON Schema from a settings model that documents every configurable option in the system. Unlike deployment configuration validation (which validates user-authored files), settings validation provides a canonical reference for the application's own configuration surface. Each setting entry includes the property name, JSON type, default value, human-readable description, and the corresponding environment variable name. This enables IDE-assisted editing of settings files, programmatic discovery of available settings, and automated documentation generation.

Usage

Apply this principle when an application has a large configuration surface (dozens or hundreds of settings) controlled by environment variables, config files, or profiles. It is essential when different deployment environments (local, staging, production, cloud) require different configurations and operators need a reliable reference for available options.

Theoretical Basis

The core mechanism is model-driven settings schema generation:

  1. Define settings as typed models: Each setting has a name, type, default, and description
  2. Annotate with environment variables: Map each setting to its `PREFECT_*` env var
  3. Generate schema: Auto-generate JSON Schema from the model definitions
  4. Distribute schema: Provide to IDEs, documentation generators, and validation tools

Pseudo-code Logic:

# Abstract schema generation algorithm
settings_model = collect_all_settings_groups()
for group in settings_model:
    for setting in group.fields:
        schema_entry = {
            "name": setting.name,
            "type": setting.type_annotation,
            "default": setting.default_value,
            "description": setting.docstring,
            "env_vars": setting.supported_environment_variables,
        }
        schema.add(group.name, schema_entry)
output_json_schema(schema)

Related Pages

Page Connections

Double-click a node to navigate. Hold to expand connections.
Principle
Implementation
Heuristic
Environment