MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models
Zhiwei Liu, Jielin Qiu, Shiyu Wang, Jianguo Zhang, Zuxin Liu, Roshan Ram, Haolin Chen, Weiran Yao, Huan Wang, Shelby Heinecke, Silvio Savarese, Caiming Xiong
2025-07-22
Summary
This paper talks about MCPEval, an open-source system that automatically creates tasks and evaluates AI models, especially large language models, to see how well they perform across different areas.
What's the problem?
The problem is that testing AI models usually requires lots of manual work to design tasks and check answers, which is slow, expensive, and can miss important ways to judge AI performance.
What's the solution?
The authors built MCPEval to automatically generate a wide variety of tasks and evaluate AI responses deeply, making the testing process faster, more thorough, and less reliant on humans.
Why it matters?
This matters because it helps researchers and developers quickly and accurately measure how good AI models are, speeding up improvements and making sure AI works well in many real-world applications.
Abstract
MCPEval is an open-source framework that automates task generation and evaluation for Large Language Models across various domains, improving the assessment process.