LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?
Guozhao Mo, Wenliang Zhong, Jiawei Chen, Xuanang Chen, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Le Sun
2025-08-06
Summary
This paper talks about LiveMCPBench, a benchmark designed to test how well large language model agents can handle many different real-world software development tasks using tools in the MCP ecosystem.
What's the problem?
The problem is that AI agents often struggle to manage and use the wide variety of tools available in real programming environments, making it hard to measure their true capabilities across many complex tasks.
What's the solution?
LiveMCPBench solves this by creating a large, varied set of tasks and a system that can evaluate agents’ performance in a scalable and adaptive way, allowing fair and detailed measuring of how well agents can navigate and solve problems in the MCP tool ecosystem.
Why it matters?
This matters because having a reliable benchmark helps researchers understand where AI agents are strong and where they need improvement, guiding future development of smarter programming assistants.
Abstract
LiveMCPBench provides a comprehensive benchmark for evaluating LLM agents across a diverse set of real-world tasks in the MCP ecosystem, using a scalable evaluation pipeline and adaptive judging framework.