@inproceedings{wang-etal-2026-mcp,
    title = "{MCP}-Flow: Facilitating {LLM} Agents to Master Real-World, Diverse and Scaling {MCP} Tools",
    author = "Wang, WenHao  and
      Niu, Peizhi  and
      Xu, Zhao  and
      Chen, Zhaoyu  and
      Du, Jian  and
      Du, Yaxin  and
      Pang, Xianghe  and
      Huang, Keduan  and
      Wang, Yanfeng  and
      Yan, Qiang  and
      Chen, Siheng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.231/",
    pages = "5087--5116",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) increasingly rely on external tools to perform complex, realistic tasks, yet their ability to utilize the rapidly expanding Model Contextual Protocol (MCP) ecosystem remains limited. Existing MCP research covers few servers, depends on costly manual curation, and lacks training support, hindering progress toward real-world deployment. To overcome these limitations, we introduce MCP-Flow, an automated web-agent-driven pipeline for large-scale server discovery, data synthesis, and model training. MCP-Flow collects and filters data from 1166 servers and 11536 tools, producing 68733 high-quality instruction-function call pairs and 6439 trajectories, far exceeding prior work in scale and diversity. Extensive experiments demonstrate MCP-Flow{'}s effectiveness in driving superior MCP tool selection, function-call generation, and enhanced agentic task performance. MCP-Flow thus provides a scalable foundation for advancing LLM agents' proficiency in real-world MCP environments."
}