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LNR Server 01: Input Data Processing MCP Server

@ayupow

リポジトリ

基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

ayupow

投稿者

ayupow

LNR Server 01: Input Data Processing MCP Server について

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 の設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

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LNR Server 01: Input Data Processing MCP Server のツール

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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 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

よくある質問

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.

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