The Model Context Protocol (MCP), introduced by Anthropic in November 2024, defines a standardized interface for connecting large language models (LLMs) to external tools, data sources, and services. Within months of release, hundreds of community-built MCP servers appeared on GitHub, but no software-maintenance literature has yet described how the ecosystem is being structured in production.
arXiv:2606. 05339v1 Announce Type: cross Abstract: MCP (Model Context Protocol) enables LLMs (Large Language Models) to interact with external tools and data sources via a standardized protocol.
By Joshua Owotogbe, Indika Kumara, Willem-Jan van den Heuvel, Damian Andrew Tamburri, Antonio Ken Iannillo, Roberto Natella
arXiv:2608. 08467v1 Announce Type: new Abstract: The Model Context Protocol (MCP) standardizes how servers expose data and tools to Large Language Models (LLMs).
By Minhan Cho, Soyoung Park, Kihyeon Jeong, Byeongkyu Jeon, Daejin Choi, Jinyoung Han
arXiv:2607. 10123v1 Announce Type: cross Abstract: The rapid emergence of the Model Context Protocol (MCP) has introduced a new standard for connecting large language models to external tools and services.
By Benny Toeppe, Amine Barrak, Emna Ksontini
Hybrid Semantic Tool Discovery for Enterprise MCP Gateway presents SCOUT, a system that addresses two major challenges in large language model (LLM) agent tool usage: a context‑engineering bottleneck and a tool discoverability barrier. SCOUT reframes tool exposure as a context‑selection problem, injecting only relevant tools into the model’s context window and providing two MCP meta‑tools—tool_search and execute_tool—to perform hybrid retrieval via BM25 and dense vector search. In production at PayPal, SCOUT cuts MCP tool‑token consumption by 99%, dramatically reducing per‑query inference cost while remaining model‑agnostic and requiring no client‑side changes.
By Olympia Saha, Amy Wang, Srinivasan Manoharan
arXiv:2606. 04769v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) has emerged as a critical standard empowering Large Language Models (LLMs) to utilize external tools.
By Yutao Shi, Xiaohan Zhang, Xiangjing Zhang, Xihua Shen, Hui Ouyang, Huming Qiu, Mi Zhang, Min Yang