Title: LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries
Primary area: datasets and benchmarks
Keywords: Agent, Tool calling, Evaluation

Abstract: 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 agents can 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 revision, 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 with the evaluated agent to produce real-time reference outputs, rather than relying on static ground-truth answers. Experiments show that even frontier LLMs achieve a task success rate below 60\%, highlighting major 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.