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LNR Server 01: Input Data Processing MCP Server

@ayupow

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

ayupow

提交者

ayupow

关于 LNR Server 01: Input Data Processing MCP Server

This server contains 6 tools. It could be used to process data for lifeline network recovery (LNR). It has been tested in a case of Shelby County.

#data-processing#network-recovery#lnr

LNR Server 01: Input Data Processing MCP Server 配置

暂无标准配置

该服务器的 README 中没有可解析的 MCP 配置块，请前往代码仓库查看安装说明。

代码仓库

LNR Server 01: Input Data Processing MCP Server 工具

工具列表暂未提供

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 LNR Server 01: Input Data Processing?

LNR Server 01: Input Data Processing is a research implementation and experimental data repository for a paper developed by XXX University in China. It focuses on operating TCG‑TE LNR agents using graph‑guided MCP tools and demonstrates agent performance with multiple large language models.

How to use LNR Server 01: Input Data Processing?

—

Key features of LNR Server 01: Input Data Processing

  • Complete implementation for paper on graph‑guided MCP tools
  • Agent operation videos under NPG‑TE and TCG‑TE patterns
  • Integration with GPT‑5, GPT‑4o, Claude sonnet 3.7, and GPT‑4.1
  • Prototype demonstration for operating and integrating MCP servers
  • Restrictive license during review; will transition to MIT post‑acceptance

Use cases of LNR Server 01: Input Data Processing

  • Research on graph‑guided MCP tool orchestration for agents
  • Experimental validation of TCG‑TE and NPG‑TE agent patterns
  • Demonstration of MCP server integration with a prototype system

常见问题

1

What is the current license?

During the paper review period, all contents in the repository are not permitted for reuse. After the paper is accepted, the repository will transition to an MIT license.

2

What are the main dependencies?

The work heavily relies on LangGraph, LangChain, Hugging Face MTEB leaderboard, NetworkX, PyTorch Geometric, and LLM providers (OpenAI, Anthropic, Qwen, Llama, etc.).

3

Who developed this server?

It was developed by XXX University in China.

4

Can I use the code for my own projects now?

No. The code is under a restrictive license until the paper is accepted. Contact the corresponding author for inquiries about academic use during the review period.

5

What models were tested with the agents?

The demonstrations use GPT‑5, GPT‑4o, Claude sonnet 3.7, and GPT‑4.1 for NPG‑TE and TCG‑TE agent patterns.

评论

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