retrieval-augmented-thinking
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An MCP server implementation that enhances AI model capabilities with structured, retrieval-augmented thinking processes.
The Retrieval-Augmented Thinking MCP Server is designed to enhance AI model capabilities by implementing structured, retrieval-augmented thinking processes. This server facilitates dynamic thought chains, parallel exploration paths, and recursive refinement cycles, which collectively improve reasoning and problem-solving capabilities. By maintaining coherent reasoning flows with adaptive thought chains, the server supports iterative hypothesis generation and context coherence across non-linear reasoning paths. It also allows for dynamic scope adjustment, enabling flexible exploration and refinement. The server includes features for real-time quality assessment of thought processes, branch management for handling parallel exploration paths, and revision tracking to manage recursive refinement cycles.
Features
- Adaptive Thought Chains: Maintains coherent reasoning flows with branching and revision capabilities.
- Iterative Hypothesis Generation: Implements validation cycles for hypothesis testing.
- Context Coherence: Preserves context across non-linear reasoning paths.
- Dynamic Scope Adjustment: Supports flexible exploration and refinement.
- Quality Assessment: Real-time evaluation of thought processes.
