Ask targeted questions to clarify user requirements and improve the prompt engineering process, ensuring accurate and optimized outputs for Claude Code.
Retrieve detailed statistics on cache systems, including hit rates, expiry times, storage usage, and performance metrics. Ideal for monitoring effectiveness, debugging issues, understanding patterns, and optimizing cache layers.
Enables AI models to interactively prompt users for input or clarification directly through their code editor. It facilitates real-time communication between assistants and users during development tasks.
Provides access to your Amplenote SQLite database cache, enabling search and retrieval of notes and tasks, including full-text search, bidirectional references, task filtering by priority and due dates, and recently modified content tracking.
Provides persistent memory and semantic code understanding for AI assistants using MongoDB Atlas Vector Search. Enables intelligent code search, memory management, and pattern detection across codebases with complete semantic context preservation.