LNR Server 01: Input Data Processing
@ayupow
About LNR Server 01: Input Data Processing
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.
Basic information
Config
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RepositoryTools
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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
FAQ from LNR Server 01: Input Data Processing
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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