MCPAgentBench: Evaluating LLM Agents with Real-World Tools
Published:Dec 31, 2025 02:09
•1 min read
•ArXiv
Analysis
This paper addresses the limitations of current LLM agent evaluation methods, specifically focusing on tool use via the Model Context Protocol (MCP). It introduces a new benchmark, MCPAgentBench, designed to overcome issues like reliance on external services and lack of difficulty awareness. The benchmark uses real-world MCP definitions, authentic tasks, and a dynamic sandbox environment with distractors to test tool selection and discrimination abilities. The paper's significance lies in providing a more realistic and challenging evaluation framework for LLM agents, which is crucial for advancing their capabilities in complex, multi-step tool invocations.
Key Takeaways
- •Introduces MCPAgentBench, a new benchmark for evaluating LLM agents' tool use.
- •Uses real-world MCP definitions and authentic tasks.
- •Employs a dynamic sandbox environment with distractors to test tool selection.
- •Provides comprehensive metrics for task completion and execution efficiency.
- •Open-source code available on Github.
Reference
“The evaluation employs a dynamic sandbox environment that presents agents with candidate tool lists containing distractors, thereby testing their tool selection and discrimination abilities.”