Datailor Preference MCP

Datailor revolutionizes how you interact with your digital assistants by externalizing your recurring corrections into local, editable preferences. Key capabilities include: * Automatically learn and store preferences from interactions * Local, human-readable preference files in Markdown * Context-aware application of preferences * Conflict detection and transparency * Agent-neutral operation across various assistants This tool addresses the common frustration of repeatedly giving the same instructions. Datailor observes your corrections, transforms them into explicit preferences, and applies them to subsequent interactions. This approach minimizes user effort, as preferences are learned as you work, eliminating the need for manual rule configuration or intensive maintenance. Your defaults are applied quietly in the background, reducing interruptions while maintaining user control. Datailor is built with a strong emphasis on user ownership and transparency. All preferences reside in plain-text Markdown files on your local machine, ensuring data privacy and freedom from vendor lock-in. You can review, edit, or remove any learned preference, understanding exactly when and why it was applied. The system is designed to be agent-agnostic, allowing your personalized standards to apply uniformly across different digital assistants, enhancing consistency and efficiency in your workflows. Ideal for individual developers, writers, or any professional who frequently uses digital assistants and seeks to personalize their output and behavior consistently. Datailor ensures your standards travel with every assistant, improving productivity and reducing repetitive instructional input.