@inproceedings{liu-etal-2026-toolscope,
    title = "{T}ool{S}cope: Enhancing {LLM} Agent Tool Use through Tool Merging and Context-Aware Filtering",
    author = "Liu, Marianne Menglin  and
      Garcia, Daniel  and
      Parllaku, Fjona  and
      Upadhyay, Vikas  and
      Shah, Fahad  and
      Roth, Dan",
    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.1573/",
    pages = "34095--34119",
    ISBN = "979-8-89176-390-6",
    abstract = "Large language model (LLM) agents rely on external tools to solve complex tasks, but real-world toolsets often contain redundant tools with overlapping names and descriptions, introducing ambiguity and reducing selection accuracy. LLMs also face strict input context limits, preventing efficient consideration of large toolsets. To address these challenges, we propose ToolScope, which includes: (1) ToolScopeMerger with Auto-Correction to automatically audit and fix tool merges, reducing redundancy, and (2) ToolScopeRetriever to rank and select only the most relevant tools for each query, compressing toolsets to fit within context limits without sacrificing accuracy. Evaluations on three state-of-the-art LLMs and three open-source tool-use benchmarks show gains of 8.38{\%} to 38.6{\%} in tool selection accuracy, demonstrating ToolScope{'}s effectiveness in enhancing LLM tool use."
}