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Adaptive Graph of Thoughts MCP Server

@SaptaDey

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基本情報

カテゴリ

その他

ライセンス

MIT

トランスポート

stdio

作者

SaptaDey

Adaptive Graph of Thoughts MCP Server について

LLM Reasoning Framework for Scientific Research

#graph-algorithms#mcp#neo4j-graph#graph-of-thoughts#mcp-server

Adaptive Graph of Thoughts MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "Adaptive-Graph-of-Thoughts-MCP-server": {
      "command": "python",
      "args": [
        "src/adaptive_graph_of_thoughts/main.py"
      ]
    }
  }
}

Adaptive Graph of Thoughts MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is 🧠 Adaptive Graph of Thoughts?

Adaptive Graph of Thoughts (AGoT) is a high-performance MCP server that implements the Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) framework. It uses a Neo4j graph database as a dynamic knowledge store and exposes reasoning capabilities through the Model Context Protocol (MCP), enabling seamless integration with AI assistants like Claude Desktop.

How to use 🧠 Adaptive Graph of Thoughts?

Clone the repository, install dependencies with Poetry, run the interactive setup wizard (poetry run python -m agt_setup), then start the server with Uvicorn (poetry run uvicorn adaptive_graph_of_thoughts.main:app --reload). For production, use Docker Compose or a Kubernetes Helm chart. Connect via MCP clients using the /mcp endpoint with Bearer authentication.

Key features of 🧠 Adaptive Graph of Thoughts

  • 8-stage graph-based reasoning pipeline with dynamic confidence scoring
  • Real-time evidence integration from PubMed, Google Scholar, and Exa Search
  • High-performance async FastAPI server backed by Neo4j
  • Native MCP support for Claude Desktop and VS Code
  • Full Docker and Kubernetes (Helm) deployment support
  • Interactive setup wizard for credentials and configuration

Use cases of 🧠 Adaptive Graph of Thoughts

  • Multi-step scientific question answering with evidence-backed reasoning
  • Hypothesis generation, pruning, and merging from research literature
  • Construction and exploration of knowledge graph connectomes from queries
  • Synthesis of conclusions with quantified confidence and audit trails
  • Cloud-native deployment for scalable scientific AI reasoning

よくある質問

1

What are the runtime dependencies for 🧠 Adaptive Graph of Thoughts?

A running Neo4j instance with the APOC library installed is required, along with Python 3.11+, Poetry, and the dependencies defined in `pyproject.toml`.

2

How is authentication handled?

All MCP API endpoints require Bearer token authentication. A Bearer token is verified by the Auth Middleware before any request reaches the GoT Processor.

3

Which external data sources does 🧠 Adaptive Graph of Thoughts integrate with?

It integrates with PubMed, Google Scholar, and Exa Search for real-time evidence retrieval during the reasoning pipeline.

4

Can 🧠 Adaptive Graph of Thoughts be deployed in containers or cloud environments?

Yes. The project provides a Dockerfile, a production `docker-compose.prod.yml`, and a Kubernetes Helm chart in the `helm/` directory for cloud-ready deployment.

5

Where does reasoning data live?

All reasoning state (nodes, relationships, sessions) is stored in a Neo4j graph database, either local or remote, as configured in `config/settings.yaml` or environment variables.

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