Design configuration parameters to be language-agnostic when possible, use established configuration objects, and maintain consistency across language server implementations. Avoid language-specific CLI options or constructor parameters when the functionality could apply broadly. Key principles: 1. **Use enum values instead of class names** for language server-specific settings to make them user-friendly 2. **Make CLI options generic** - use `--ls-max-memory` instead of `--max-memory` for Intelephense-specific features 3. **Leverage existing configuration objects** like `LanguageServerConfig` instead of adding new constructor parameters 4. **Handle configuration evolution properly** - force user decisions on new settings rather than assuming defaults, and provide clear error messages for missing required keys Example of good configuration design: ```python # Good: Use enum value for LS-specific settings custom_settings = self._solidlsp_settings.ls_specifics.get("intelephense", {}) # Good: Generic CLI option that could apply to multiple language servers @click.option("--ls-max-memory", type=int, help="Maximum memory (MB) for language server") # Good: Use existing config object def __init__(self, config: LanguageServerConfig, logger: LanguageServerLogger, ...): max_memory = config.max_memory # Instead of separate parameter # Good: Force user decision on new config if "ignore_all_files_in_gitignore" not in config_dict: raise ValueError("Missing required config: ignore_all_files_in_gitignore") ``` This approach ensures configuration remains maintainable, user-friendly, and consistent as the codebase grows across multiple language servers.