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awesome-mcp

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.

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Jan 8, 2026
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Jan 9, 2026

Awesome MCP - Model Context Protocol

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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, 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

January 9, 2026 at 02:10:40 AM UTC

Theorem

Papers (45)

Library

Tutorial

Written Tutorials

Video Tutorials

Contributing

We welcome contributions to this repository! If you have a resource that you believe should be included, please submit a pull request or open an issue. Contributions can include:

  • New libraries or tools related to MCP.
  • Tutorials or guides that help users understand and implement MCP.
  • Research papers that advance the field of MCP.
  • Any other resources that you find valuable for the community

How to Contribute

  1. Fork the repository.
  2. Create a new branch for your changes.
  3. Make your changes and commit them with a clear message.
  4. Push your changes to your forked repository.
  5. Submit a pull request to the main repository.

Before contributing, take a look at the existing resources to avoid duplicates.

License

This repository is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material, provided you give appropriate credit, link to the license, and indicate if changes were made.

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