Kazem Faghih, Wenxiao Wang, Yize Cheng, Siddhant Bharti, Gaurang Sriramanan, Sriram Balasubramanian, Parsa Hosseini, Soheil Feizi
| Challenge: | Large language models (LLMs) can now access a wide range of external tools thanks to the Model Context Protocol (MCP). |
| Approach: | They expose a vulnerability in prevalent tool/function-calling protocols by editing tool descriptions to find out which tools are used by LLMs. |
| Outcome: | The proposed changes in the tool descriptions can increase the usage of tools from LLMs when competing with alternatives. |
Similar Papers
ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering (2026.acl-long)
Copied to clipboard
| Challenge: | Large language model (LLM) agents often face strict input context limits, preventing efficient consideration of large toolsets. |
| Approach: | They propose a tool that allows LLMs to merge tools with auto-correction and toolScopeRetriever to rank and select only the most relevant tools for each query. |
| Outcome: | 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. |
ACEBench: A Comprehensive Evaluation of LLM Tool Usage (2025.findings-emnlp)
Copied to clipboard
Chen Chen, Xinlong Hao, Weiwen Liu, Xu Huang, Xingshan Zeng, Shuai Yu, Dexun Li, Yuefeng Huang, Xiangcheng Liu, Wang Xinzhi, Wu Liu
| Challenge: | Existing benchmarks for evaluating LLMs’ tool usage face several limitations: limited evaluation scenarios, lacking assessments in real multi-turn dialogue contexts; narrow evaluation dimensions, with insufficient detailed assessments of how LLM use tools; and reliance on LLM or real API executions for evaluation, which introduces significant overhead. |
| Approach: | ACEBench is a benchmark for evaluating tool usage in Large Language Models . it categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent. |
| Outcome: | ACEBench categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent. |
FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments (2026.findings-acl)
Copied to clipboard
Amir Saeidi, Venkatesh Mishra, Souradeep Mukhopadhyay, Gaowen Liu, Ali Payani, Jayanth Srinivasa, Chitta Baral
| Challenge: | Large Language Models are being increasingly deployed as decision-making core of autonomous agents . however, in conversational benchmarks, these agents fail due to the cascading effects of incorrect decision- making . |
| Approach: | They propose a framework that analyzes failure trajectories from baseline agents to identify most prevalent errors. |
| Outcome: | Experiments show that the framework improves performance over open-source LLMs . the framework can be used to build reliable, multi-turn tool-use agents . |
SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have a profound impact on a wide range of applications. |
| Approach: | They propose a framework to mitigate the tool-abuse behavior of Large Language Models and propose SMARTCAL to mitigate this issue. |
| Outcome: | The proposed framework improves the performance of LLMs on three datasets with two mainstream tool-use frameworks and shows an 8.6% increase in QA performance and 21.6 percent lower expected calibration error (ECE) than existing methods. |
A Joint Optimization Framework for Enhancing Efficiency of Tool Utilization in LLM Agents (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing efforts for tool utilization involve an LLM agent that contains instructions on using the description of the available tools to determine and call the tools required to solve the problem. |
| Approach: | They propose to optimize the context of LLM agents by combining the instructions provided in agent prompts and tool descriptions to enhance their interaction. |
| Outcome: | The proposed framework improves both the instructions provided in agent prompt and tool description, enhancing their interaction. |
Towards Preference Following in Tool Calling Language Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Currently, large language model (LLM)-based agents can't follow user preferences when calling tools. |
| Approach: | They propose a benchmark to evaluate agents' ability to identify personalized user preferences from interaction histories and to adhere to these preferences when calling tools. |
| Outcome: | The proposed model achieves 51.16% accuracy on the APOLLO benchmark, while GPT-4o achieves only 51.13% accuracy. |
ToolSpectrum: Towards Personalized Tool Utilization for Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches focus on functional tool selection following user instructions while overlooking the critical role of context-aware personalization in tool selection. |
| Approach: | They propose a benchmark to evaluate LLMs’ capabilities in personalized tool utilization. |
| Outcome: | The proposed benchmark evaluates LLMs' capabilities in personalized tool utilization. |
Middleware for LLMs: Tools Are Instrumental for Language Agents in Complex Environments (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are generalist agents capable of operating within complex environments. |
| Approach: | They propose a class of tools that can serve as a middleware layer shielding LLMs from environmental complexity. |
| Outcome: | The proposed tool can shield the LLM from environmental complexity in two representative complex environments. |
Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems (2025.findings-emnlp)
Copied to clipboard
| Challenge: | emergence of tool agent paradigm has broadened capability boundaries of the Large Language Model (LLM) but effectiveness of tool agents limited due to parameter failure during execution . |
| Approach: | They propose a parameter failure taxonomy to investigate parameter failure . they propose suggestions for standardizing tool return formats and improving error feedback mechanisms . |
| Outcome: | The proposed model is based on a tool agent invocation chain and a mainstream tool agent . it shows that parameter name hallucination failure stems from inherent limitations . |
LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error (2024.acl-long)
Copied to clipboard
| Challenge: | Existing work on tool-augmented LLMs focuses on the broad coverage of tools and the flexibility of adding new tools. |
| Approach: | They propose a biologically inspired method for tool-augmented LLMs that orchestrates three key mechanisms for successful tool use behaviors in the biological system: trial and error, imagination, and memory. |
| Outcome: | The proposed method improves tool learning for LLMs under both in-context learning and fine-tuning settings, bringing a boost of 46.7% to Mistral-Instruct-7B and outperforms GPT-4. |