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ai-in-transportation-lab/awesome-mcp

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A carefully curated collection of high-quality tools, libraries, research papers, projects, and tutorials centered around Model Context Protocol (MCP) — a novel paradigm designed to enable modular,...

概览

A carefully curated collection of high-quality tools, libraries, research papers, projects, and tutorials centered around Model Context Protocol (MCP) — a novel paradigm designed to enable modular, adaptive coordination between large language models (LLMs) and external tools or data contexts. This repository serves as a comprehensive, well-organized knowledge hub for researchers and developers exploring the next frontier of interactive, context-aware AI systems. The MCP framework facilitates fine-grained orchestration of model behavior through structured, tool-integrated communication protocols. It supports advanced workflows like adaptive reasoning, multi-tool routing, contextual memory access, and iterative refinement. By treating models not as static endpoints but as participants in a dynamic co-mining or co-creation loop, MCP pushes the boundaries of what intelligent systems can achieve.

README

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Awesome MCP - Model Context Protocol

A carefully curated collection of high-quality tools, libraries, research papers, projects, and tutorials centered around Model Context Protocol (MCP) — a novel paradigm designed to enable modular, adaptive coordination between large language models (LLMs) and external tools or data contexts. This repository serves as a comprehensive, well-organized knowledge hub for researchers and developers exploring the next frontier of interactive, context-aware AI systems.

The MCP framework facilitates fine-grained orchestration of model behavior through structured, tool-integrated communication protocols. It supports advanced workflows like adaptive reasoning, multi-tool routing, contextual memory access, and iterative refinement. By treating models not as static endpoints but as participants in a dynamic co-mining or co-creation loop, MCP pushes the boundaries of what intelligent systems can achieve.

To keep the community up-to-date with the latest developments, this repository is continuously enriched with newly published MCP-related papers, real-world use cases, and open-source implementations. From LangGraph-based architectures to custom tool routers and model-control interfaces, the collection aims to highlight both foundational ideas and emerging best practices.

[!NOTE] 📢 Announcement: Our paper is now available on arXiv!
Title: Model Context Protocols in Adaptive Transport Systems: A Survey
If you find this paper interesting, please consider citing our work. Thank you for your support!

@article{chhetri2025model,
  title={Model Context Protocols in Adaptive Transport Systems: A Survey},
  author={Chhetri, Gaurab and Somvanshi, Shriyank and Islam, Md Monzurul and Brotee, Shamyo and Mimi, Mahmuda Sultana and Koirala, Dipti and Pandey, Biplov and Das, Subasish},
  journal={arXiv preprint arXiv:2508.19239},
  year={2025}
}

Whether you are building AI agents, researching model-tool alignment, or experimenting with novel retrieval-augmented generation pipelines, this resource offers a centralized, evolving platform to explore the powerful and expanding universe of MCP-enabled systems.

Last Updated

November 2, 2026 at 05:29:12 AM UTC

Theorem

Papers (186)

在 GitHub 查看完整 README

安装

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Open the repository installation guide 新手?先看 mcpskill.org 教程 →