mlsanityData to 14 Oct 2026

ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMs

arXiv 2411.13547 ↗First posted 20 Nov 2024cs.SE cs.AIcited by 18

On the map: LLM agents and tool use → Tool learning and function calling

Abstract (arXiv)

Evaluating Large Language Models (LLMs) is one of the most critical aspects of building a performant compound AI system. Since the output from LLMs propagate to downstream steps, identifying LLM errors is crucial to system performance. A common task for LLMs in AI systems is tool use. While there are several benchmark environments for evaluating LLMs on this task, they typically only give a success rate without any explanation of the failure cases. To solve this problem, we introduce TOOLSCAN, a new benchmark to identify error patterns in LLM output on tool-use tasks. Our benchmark data set comprises of queries from diverse environments that can be used to test for the presence of seven newly characterized error patterns. Using TOOLSCAN, we show that even the most prominent LLMs exhibit these error patterns in their outputs. Researchers can use these insights from TOOLSCAN to guide their error mitigation strategies.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 17 of its 24 references that are in the map. First posted on arXiv 20 Nov 2024.

Builds on

17 of 24 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models12 Mar
  2. 2024ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios1 Jan
  3. 2023MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback19 Sep
  4. 2023Gorilla: Large Language Model Connected with Massive APIs24 May

Other references · 13

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  5. 2022Emergent Abilities of Large Language Modelscited by 2,512
  6. 2023Chameleon: Plug-and-Play Compositional Reasoning with Large Language Modelscited by 356
  7. 2024AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agentscited by 339
  8. 2024Long-form factuality in large language modelscited by 155
  9. 2024xLAM: A Family of Large Action Models to Empower AI Agent Systemscited by 111
  10. 2022DDXPlus: A New Dataset For Automatic Medical Diagnosiscited by 88
  11. 2023MetaAgents: Large Language Model Based Agents for Decision-Making on Teamingcited by 85
  12. 2024AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent Systemcited by 53
1 more
  1. 2018Task Transfer by Preference-Based Cost Learningcited by 18

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 8; 2026: 10.

18 papers.

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  1. 2025MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Serverscited by 109
  2. 2025TRAIL: Trace Reasoning and Agentic Issue Localizationcited by 104
  3. 2026AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Conditioncited by 23
  4. 2025PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Casescited by 22
  5. 2026Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?cited by 10
  6. 2025FinGAIA: A Chinese Benchmark for AI Agents in Real-World Financial Domaincited by 9
  7. 2025WebSeer: Training Deeper Search Agents through Reinforcement Learning with Self-Reflectioncited by 7
  8. 2025LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedbackcited by 6
  9. 2026Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environmentscited by 6
  10. 2025Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templatescited by 4
  11. 2026Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agentscited by 3
  12. 2026Open, Reliable, and Collective: A Community-Driven Framework for Tool-Using AI Agentscited by 1
6 more
  1. 2026Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Executioncited by 1
  2. 2025Repairing Tool Calls Using Post-tool Execution Reflection and RAGcited by 0
  3. 2026AgentFloor: How Far Up the tool use Ladder Can Small Open-Weight Models Go?cited by 0
  4. 2026No Action Without a NOD: A Heterogeneous Multi-Agent Architecture for Reliable Service Agentscited by 0
  5. 2026Noise Floor Audit for Agent Benchmarkscited by 0
  6. 2026Lost in the Request: How Communication Variation Disrupts Retrieval and Action in Email Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Failure Modes in LLM Systems: A System-Level Taxonomy for Reliable AI Applications
  2. 2026ToolFailBench: Diagnosing Tool-Use Failures in LLM Agents
  3. 2026ToolMenuBench: Benchmarking Tool-Menu Filtering Strategies for Reliable and Efficient LLM Agents
  4. 2026EvalBuilder: Can LLMs Build Efficient Benchmarks?
  5. 2025CLEAR: Error Analysis via LLM-as-a-Judge Made Easy
  6. 2026The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs
  7. 2024Large Language Model Evaluation Via Multi AI Agents: Preliminary results
  8. 2025CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios