ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910202266124288 |
|---|---|
| author | Liu, Marianne Menglin Garcia, Daniel Parllaku, Fjona Upadhyay, Vikas Shah, Syed Fahad Allam Roth, Dan |
| author_facet | Liu, Marianne Menglin Garcia, Daniel Parllaku, Fjona Upadhyay, Vikas Shah, Syed Fahad Allam Roth, Dan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20036 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering Liu, Marianne Menglin Garcia, Daniel Parllaku, Fjona Upadhyay, Vikas Shah, Syed Fahad Allam Roth, Dan Computation and Language Software Engineering 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. |
| title | ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering |
| topic | Computation and Language Software Engineering |
| url | https://arxiv.org/abs/2510.20036 |