LNR Server 01: Input Data Processing MCP Server

@ayupow

Basic information

About 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 config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

LNR Server 01: Input Data Processing MCP Server tools

Tool list not available yet

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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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?

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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

Frequently asked questions

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.

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.).

Who developed this server?

It was developed by XXX University in China.

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.

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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