Adaptive Graph of Thoughts

Processes complex scientific queries through an 8-stage Graph-of-Thoughts pipeline that decomposes questions into hypotheses, gathers evidence, builds knowledge graphs in Neo4j, and provides confidence-scored conclusions with bias detection and falsifiability assessment for systematic research analysis.

This MCP server provides advanced scientific reasoning capabilities through an 8-stage Graph-of-Thoughts pipeline that processes complex queries by decomposing them into hypotheses, gathering evidence, and building knowledge graphs in Neo4j. Built by the Adaptive Graph of Thoughts Development Team using Python with FastAPI, Neo4j, and the official MCP SDK, it implements stages for initialization, decomposition, hypothesis generation, evidence gathering, pruning/merging, subgraph extraction, composition, and reflection with confidence scoring across empirical support, theoretical basis, methodological rigor, and consensus alignment. The implementation supports both HTTP and STDIO transports, includes Docker deployment with Neo4j integration, and features bias detection, falsifiability assessment, and Bayesian confidence updates, making it valuable for researchers conducting systematic literature reviews, scientists analyzing complex interdisciplinary questions, and AI assistants that need structured reasoning with transparent confidence assessments for evidence-based decision making.