Iterative Refinement

The Iterative Refinement MCP server allows you to systematically improve text, ideas, or algorithms through continuous self-evaluation. It avoids standard LLM timeouts by breaking the refinement process into discrete, trackable steps. Key Features: Iterative Refinement: Follows a structured Draft → Critique → Revise → Converge workflow. Mathematical Convergence: Uses cosine similarity to measure when refinement is complete, ensuring optimal results without endless loops. Domain-Specific Optimization: Auto-detects and optimizes for technical, marketing, strategy, legal, and financial domains. Progress Visibility: Each step returns immediately, allowing for real-time UI updates and transparent progress tracking. Parallel Processing: Supports multiple concurrent refinement sessions and parallel critiques per iteration. AI-Friendly Error Handling: Provides actionable diagnostics and recovery hints directly to your AI assistant.