MSC-Bench: A Rigorous Benchmark for Multi-Server Tool Orchestration
This provides a diagnostic framework for developing more capable tool-using agents, though it is incremental as it builds on existing benchmark efforts.
The paper tackles the problem of evaluating multi-server tool orchestration by LLM agents, introducing MSC-Bench as a benchmark that addresses gaps in existing evaluations, revealing that rigid hierarchies hinder performance and state-of-the-art agents have systemic weaknesses in robustness.
We introduce MSC-Bench, a large-scale benchmark for evaluating multi-hop, end-to-end tool orchestration by LLM agents in a hierarchical Model-Context Protocol (MCP) ecosystem. Existing benchmarks often evaluate tools in isolation, ignoring challenges such as functional overlap and cross-server orchestration, leading to overly optimistic assessments. MSC-Bench addresses these gaps by constructing ground truth through 'equal function sets', allowing objective metrics such as F1 score and reducing the dependency on LLM-as-a-judge evaluation. Organized as a five-level curriculum, it systematically tests agent capabilities from single-tool orchestration to complex cross-server planning, and robustness to out-of-scope requests. Experiments reveal that rigid hierarchies can hinder performance without co-designed strategies, and even state-of-the-art agents exhibit systemic weaknesses in robustness. MSC-Bench provides a diagnostic framework to expose these limitations and guide the development of more capable and efficient tool-using agents. The benchmark and resources are publicly available at https://github.com/snooow1029/MSC_Bench.