LNR-server-02-cascading-failure-scenario-simulatio

# Large language model (LLM) agents based on tool chian generation (TCG)-tool execution (TE) pattern with graph-guided model context protocol (MCP) tools ## ⚠️ 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 snippet of using the **prototype** to operate the TCG-TE LNR agents using graph-guided MCP tools ↓↓↓ A snippet of using the **prototype** to integrate a new server The full video to showcase the prototype and server updating can be found in: ### 2.2 Operation of agents based on NPG-TE pattern ↓↓↓ A snippet of operating the **NPG-TE agents with discrete MCP tools driven by GPT-5**. ↓↓↓ A snippet of operating the **NPG-TE agents with discrete MCP tools driven by GPT-4o**. The full video can be found here () ### 2.3 Operation of agents based on TCG-TE pattern ↓↓↓ A snippet of operating the **TCG-TE agents with graph-guided MCP tools driven by Claude sonnet 3.7**. ↓↓↓ A snippet of operating the **TCG-TE agents with graph-guided MCP tools driven by GPT-5**. ↓↓↓ A snippet of operating the **TCG-TE agents with graph-guided MCP tools driven by GPT-4.1**. The full video can be found here () ## 3. Repository Structure ## 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.