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Adaptive Graph of Thoughts MCP Server

@SaptaDey

代码仓库主页

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

SaptaDey

关于 Adaptive Graph of Thoughts MCP Server

LLM Reasoning Framework for Scientific Research

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

Adaptive Graph of Thoughts MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Adaptive Graph of Thoughts MCP Server 工具

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当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

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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