ToolBench - Tool Usage Benchmark

LLM Tool Usage Evaluation Benchmark and Dataset T APIs & Messaging

Basic Information

  • Type: LLM Tool Usage Evaluation Benchmark and Dataset
  • Main Versions: ToolBench (SambaNova), ToolLLM/ToolBench (Tsinghua University)
  • GitHub: https://github.com/sambanova/toolbench
  • Paper: "ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs" (2024)
  • Latest Extensions: MCPToolBench++ (August 2025), WildToolBench, ToolBench2

Paradigm Description

ToolBench is a benchmark suite and dataset for evaluating LLM tool usage capabilities. It covers tasks ranging from simple single-tool calls to complex multi-step, multi-tool combinations, serving as a standardized evaluation framework for measuring LLM tool usage proficiency. ToolBench and its subsequent versions have driven systematic research and advancements in the field of tool usage, giving rise to next-generation evaluation protocols such as StableToolBench and MCPToolBench++.

Core Components

  • ToolBench Benchmark: Basic LLM tool operation capability evaluation suite
  • ToolLLM: Tool usage training framework supporting 16,000+ real-world APIs
  • ToolBench2: Upgraded version extending to compositional and multi-turn tool usage
  • StableToolBench: Stable evaluation protocol simulating reproducible APIs
  • MCPToolBench++: Released in August 2025, evaluating tool usage in MCP contexts
  • WildToolBench: Evaluation benchmark based on real user behavior patterns

Key Evaluation Findings (2025-2026)

  • Tool-MVR: Reflection-enhanced LLM sets new records (accuracy +24%, error recovery rate 58.9%)
  • WildToolBench: No model accuracy exceeds 15% among 57 LLM evaluations
  • ToolComp: Average accuracy of multi-step planning below 50%
  • SafeToolBench: Safety evaluation recall rate reaches 83%
  • PALADIN: Improves tool failure recovery rate from ~32-33% to ~90%

Evaluation Dimensions

  • Single Tool Call Accuracy: Basic tool invocation capability
  • Multi-step Planning: Planning capability for complex multi-step tool calls
  • Error Recovery: Recovery capability after tool call failures
  • Safety: Safety evaluation of tool calls
  • Real-world Scenarios: Evaluation based on real user behavior

Industry Impact

  • Scale Labs Leaderboard: Enterprise-level agent tool usage leaderboard
  • MirrorAPI: Next-generation simulated API evaluation infrastructure
  • Large Action Model: Drives research on scaling and fine-tuning large action models
  • Research Direction Guidance: Reveals key weaknesses and improvement directions for LLM tool usage

Relationship with OpenClaw Ecosystem

ToolBench provides a standardized evaluation framework for OpenClaw's agent tool usage capabilities. OpenClaw should utilize ToolBench and its variants to test and evaluate individual agents' tool invocation capabilities, with particular focus on multi-step planning accuracy and error recovery rates. The sub-15% accuracy ceiling revealed by WildToolBench also reminds OpenClaw to incorporate sufficient error handling and fallback mechanisms in agent design.