deliberate-reasoning-engine and Adaptive-Graph-of-Thoughts-MCP-server
About deliberate-reasoning-engine
haasonsaas/deliberate-reasoning-engine
MCP server that transforms linear AI reasoning into structured, auditable thought graphs
Implements semantic thought categorization (objectives, hypotheses, assumptions, evidence, critiques) as nodes in a directed acyclic graph with explicit dependency tracking and cascade invalidation—when an assumption is disproven, all dependent reasoning automatically becomes stale. Integrates with Claude Desktop via the Model Context Protocol, exposing tools for logging structured thoughts, retrieving the reasoning graph, and invalidating assumptions to maintain reasoning consistency.
About Adaptive-Graph-of-Thoughts-MCP-server
SaptaDey/Adaptive-Graph-of-Thoughts-MCP-server
LLM Reasoning Framework for Scientific Research
This tool helps scientific researchers efficiently tackle complex scientific questions. You input a scientific question, and it delivers a comprehensive, evidence-backed answer by breaking down the query, generating hypotheses, and integrating findings from sources like PubMed and Google Scholar. It's designed for scientists, academics, and researchers who need to synthesize information and form robust conclusions.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work