Dev
Dummy Config File Generator
Every new service starts the same way: someone hand-writes a placeholder config, leaves fields empty, and the test breaks on names that don't match reality. A dummy config file generator gives you a structurally correct file instantly. Choose app type — API, web server, database, or logging — and format — JSON, YAML, or TOML — and get a config with realistic defaults: plausible ports, pool sizes, log rotation settings, and rate limits. The `appType` input determines which fields appear. API produces base_url, rate_limit_rpm, jwt_expiry_seconds, and cors_origins. Database produces pool_min, pool_max, and timeout settings. Webserver produces workers and ssl_enabled. The `format` input converts the same config between JSON, YAML, and TOML — useful when evaluating formats or building a multi-format parser.
Read the complete guide — 4 min read
Added May 2026
How to use
- Choose your options above
- Click Generate
- Copy your result
Detailed instructions
- Select your target format (JSON, YAML, or TOML) from the Config Format dropdown.
- Choose the app type that matches your use case — API, web server, database, or logging.
- Click Generate to produce a realistic, syntax-valid configuration file.
- Copy the output directly or download it, then rename it to match your project's expected config filename.
- Replace placeholder values like hostnames, ports, and credentials with your environment-specific settings.
Use Cases
- •Seeding a new Express or FastAPI microservice repo with a ready-to-edit JSON config before writing any application code
- •Testing a Go or Python YAML parser against a realistic nested config with mixed types — strings, booleans, arrays, and maps
- •Generating TOML fixture files for Rust integration tests that load config via the `config` or `figment` crate on startup
- •Creating sample configuration files for open-source project README docs so contributors have a concrete reference to copy from
- •Comparing JSON, YAML, and TOML representations of the same API or database config when evaluating formats for a new service
Tips
- →Generate the same app type in all three formats back-to-back to use as a syntax reference when learning TOML or writing a multi-format parser.
- →If your parser fails on the output, the bug is almost certainly in your parser — the generated syntax is deliberately valid and clean.
- →For onboarding docs, generate a JSON config and annotate each field inline; the realistic values give new developers context that abstract placeholders don't.
- →Use the database config output as a starting schema when designing a config validation library — it covers the field types most parsers need to handle.
- →Pair the generated file with a schema validator (like Zod, Joi, or Cerberus) to immediately test both the config structure and your validation rules together.
- →When bootstrapping a monorepo, generate one config per service type and diff them — it quickly surfaces which fields should be shared versus service-specific.
FAQ
can I use the generated config file as a starting point for a real project
Yes, with edits. The output is a well-structured first draft covering the common fields for your chosen app type — swap out hostnames, adjust pool sizes or rate limits to your actual requirements, and replace any hardcoded credential values with environment variable references before committing. The structure and field names are realistic enough to use as-is for most services.
what is the difference between the json yaml and toml output formats
All three formats represent the same underlying config, but suit different ecosystems. JSON is universal and native to Node.js tooling. YAML dominates Kubernetes manifests and CI/CD pipelines like GitHub Actions. TOML is the standard for Rust (Cargo.toml), Python (pyproject.toml), and Hugo. Use this generator to view the same config in all three formats when evaluating which one to adopt or when writing a multi-format parser.
does the generated yaml pass a strict linter like yamllint
Yes. The YAML output follows correct indentation, quotes strings that could be misread as booleans, and avoids trailing whitespace — the main checks yamllint applies by default. You can pipe the output directly into yamllint or load it with PyYAML's safe_load without preprocessing.
which app type should I pick for a microservice that exposes a REST API
Choose API. That type generates fields like base_url with an environment-aware hostname, port, rate_limit_rpm, auth_enabled, jwt_expiry_seconds, and cors_origins — the fields a REST service actually needs at runtime. The webserver type focuses on static file serving and worker counts, which is better suited to a reverse proxy like Nginx rather than an application server.
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