Adaptive-Graph-of-Thoughts-MCP-server

Adaptive-Graph-of-Thoughts-MCP-server

3.4

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Adaptive Graph of Thoughts is a next-generation AI reasoning framework designed to enhance scientific research through intelligent graph structures.

Adaptive Graph of Thoughts leverages a Neo4j graph database to perform sophisticated scientific reasoning, with graph operations managed within its pipeline stages. It implements the Model Context Protocol (MCP) to integrate with AI applications like Claude Desktop, providing an Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) framework designed for complex research tasks. The system is built with modern Python and FastAPI for high performance and is Dockerized for easy deployment. It features a modular design for extensibility and customization, and connects to external databases such as PubMed, Google Scholar, and Exa Search for real-time evidence gathering. The framework is designed to process complex scientific queries using graph-based reasoning and offers dynamic confidence scoring with multi-dimensional evaluations.

Features

  • Process complex scientific queries using graph-based reasoning
  • Dynamic confidence scoring with multi-dimensional evaluations
  • Connects to external databases for real-time evidence gathering
  • Built with modern Python and FastAPI for high performance
  • Modular design for extensibility and customization

Tools

  1. scientific_reasoning_query

    Advanced scientific reasoning with graph analysis

  2. analyze_research_hypothesis

    Hypothesis evaluation with confidence scoring

  3. explore_scientific_relationships

    Concept relationship mapping

  4. validate_scientific_claims

    Evidence-based claim validation