An MCP server that uses Claude 3.5 Sonnet to transform ordinary prompts into structured, professionally engineered instructions for any LLM. It enhances AI interactions by adding context, requirements, and structural clarity to raw user inputs.
An MCP server that automatically enhances user prompts by applying advanced engineering techniques like chain-of-thought and few-shot reasoning based on identified intent. It optimizes technique selection through local learning and integrates directly into Claude sessions to improve output quality without additional API costs.
An MCP server for Claude Code that catches vague prompts, applies triage and correction pattern learning, and provides semantic search, cross-service contracts, and scorecards to reduce wasted tokens.
This MCP server provides research-backed prompt optimization tools and professional domain templates designed to improve AI performance through strategies like Tree of Thoughts and Medprompt. It enables users to analyze, auto-optimize, and refine prompts using advanced reasoning patterns and safety-critical alignment techniques.
An MCP server that provides Claude with adjustable 0-10 sliders for behavior dimensions like rigor, verification, and autonomy, allowing external tuning without modifying prompts.
