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LNR-server-03-critical-facility-identification

A specialized MCP server designed to identify critical facilities within lifeline networks (LN), specifically tested using Shelby County infrastructure data as a case study for resilient network analysis.

Updated
Jan 6, 2026

📣 Important Notice

⚠️ As the paper is under review, all contents in this repository are currently not permitted for reuse by anyone until this announcement is removed. Thank you for your understanding! 🙏

1. Overview & Objectives

This repository contains the complete implementation, experimental data, and supplementary results for the paper ××× developed by XXX University in China, and .

Pending publication, the code is shared under a restrictive license. Once the paper is accepted, the repository will transition to a MIT license. Please contact the corresponding author for any inquiries regarding academic use during the review period.

2. Videos of agents operation

2.1 Operation of the developed prototype

↓↓↓ A demonstration of using the developed prototype to operate the TCG-TE LNR agents using graph-guided MCP tools

The full video could be found here 屏幕截图 2026-01-03 201222

↓↓↓ A demonstration of using the developed prototype to integrate a new MCP server to TCG-TE LNR agents

The full video could be found here 屏幕截图 2026-01-03 201211

2.2 Operation of agents based on NPG-TE pattern

↓↓↓ A snippet of the operation of NPG-TE agent with discrete MCP tools driven by GPT-5.

↓↓↓ A screenshot of Agent's response image

The full video can be found here 屏幕截图 2026-01-03 201138

↓↓↓ A snippet of the operation of NPG-TE agents with discrete MCP tools driven by GPT-4o.

↓↓↓ A screenshot of Agent's response image

The full video can be found here 屏幕截图 2026-01-03 201102

2.3 Operation of agents based on TCG-TE pattern

↓↓↓ A snippet of the operation of TCG-TE agents with graph-guided MCP tools driven by Claude sonnet 3.7. 12月30日 (1)

The full video can be found here 屏幕截图 2026-01-03 201151

↓↓↓ A snippet of the operation of TCG-TE agents with graph-guided MCP tools driven by GPT-4.1.

↓↓↓ A screenshot of Agent's response image

The full video can be found here 屏幕截图 2026-01-03 201201

3. Repository Structure

image

4. Acknowledgments

This work heavily relies on excellent open-source projects, including but not limited to:

  • LangGraph & LangChain
  • Hugging Face MTEB leaderboard
  • NetworkX, PyTorch Geometric, and numerous LLM providers (OpenAI, Anthropic, Qwen, Llama, etc.)

We are deeply grateful to all contributors of these foundational work.

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