LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on
  Challenging Queries
Authors: name: Ming Yin, name: Dinghan Shen, name: Silei Xu, name: Jianbing Han, name: Sixun Dong, name: Mian Zhang, name: Yebowen Hu, name: Shujian Liu, name: Simin Ma, name: Song Wang, name: Sathish Reddy Indurthi, name: Xun Wang, name: Yiran Chen, name: Kaiqiang Song
Organization: Zoom AI

Tool calling has emerged as a critical capability for AI agents to interact
with the real world and solve complex tasks. While the Model Context Protocol
(MCP) provides a powerful standardized framework for tool integration, there is
a significant gap in benchmarking how well AI agents can effectively solve
multi-step tasks using diverse MCP tools in realistic, dynamic scenarios. In
this work, we present LiveMCP-101, a benchmark of 101 carefully curated
real-world queries, refined through iterative LLM rewriting and manual review,
that require coordinated use of multiple MCP tools including web search, file
operations, mathematical reasoning, and data analysis. Moreover, we introduce a
novel evaluation approach that leverages ground-truth execution plans rather
than raw API outputs, better reflecting the evolving nature of real-world
environments. Experiments show that even frontier LLMs achieve a success rate
below 60\%, highlighting major challenges in tool orchestration. Detailed
ablations and error analysis further reveal distinct failure modes and
inefficiencies in token usage, pointing to concrete directions for advancing
current models. LiveMCP-101 sets a rigorous standard for evaluating real-world
agent capabilities, advancing toward autonomous AI systems that reliably
execute complex tasks through tool use.