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arXiv · 2508.15760

LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries

Ming Yin·Dinghan Shen·Silei Xu·Sixun Dong·Mian Zhang·Yebowen Hu·Shujian Liu·Jianbing Han·Simin Ma·Song Wang·Sathish Reddy Indurthi·Xun Wang·Yiran Chen·Kaiqiang Song
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Abstract

Tool calling has emerged as a critical capability for AI agents. In contrast to conventional tool calling frameworks that rely on static, provider-specific tool definitions, the Model Context Protocol (MCP) offers a unified interface to discover and invoke tools dynamically. However, there is a significant gap in benchmarking multi-step tasks using diverse MCP tools in realistic, dynamic scenarios. In this work, we present LiveMCP-101, a benchmark of 101 real-world queries that require coordinated use of multiple MCP tools. To address temporal variability in real-world tool responses, we introduce a parallel evaluation framework where a reference agent executes a validated plan simultaneously to produce real-time reference outputs. Experiments show that even frontier LLMs achieve a success rate below 60\%, highlighting challenges in multi-step tool use. Comprehensive error analysis identifies seven failure modes spanning tool planning, parameterization, and output handling, pointing to concrete directions for improving current models. LiveMCP-101 sets a rigorous standard for evaluating real-world agent capabilities, advancing toward autonomous agent systems that reliably execute complex tasks through MCP tool orchestration.

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Ming Yin, Dinghan Shen, Silei Xu, Sixun Dong, Mian Zhang, Yebowen Hu, Shujian Liu, Jianbing Han, Simin Ma, Song Wang, Sathish Reddy Indurthi, Xun Wang, Yiran Chen, Kaiqiang Song. 2026-05-22. LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries. https://arxiv.org/abs/2508.15760

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