New Benchmark Provides Real-World Evaluation of LLM Tool-Use

Global: New Benchmark Evaluates LLM Tool-Use Capabilities

Researchers including Wenrui Liu, Zixiang Liu, Elsie Dai, Wenhan Yu, Lei Yu and Tong Yang have introduced a benchmark designed to assess large language model (LLM) agents’ ability to employ external tools via the Model Context Protocol (MCP). The work, submitted to arXiv on 31 December 2025, aims to provide a more realistic evaluation framework for autonomous AI systems that interact with toolsets.

Benchmark Overview

The proposed MCPAgentBench draws on authentic MCP definitions and compiles a dataset of real-world tasks paired with simulated MCP tools. By grounding the benchmark in genuine use cases, the authors seek to overcome the reliance on external MCP services that has limited prior evaluation sets.

Evaluation Environment

To test agents, the authors created a dynamic sandbox that presents a list of candidate tools, including distractors, for each task. This setup challenges models to correctly select and discriminate among available tools, reflecting the decision‑making complexity encountered in practical deployments.

Metrics Introduced

Beyond simple success rates, the benchmark incorporates metrics for task completion and execution efficiency, allowing a nuanced view of how quickly and accurately agents can orchestrate multi‑step tool invocations.

Experimental Findings

Experiments with several leading LLMs revealed marked performance gaps, particularly on tasks requiring complex, sequential tool use. Some models achieved high completion rates but incurred substantial latency, while others struggled to select appropriate tools altogether.

Open-Source Availability

All code associated with MCPAgentBench has been released on GitHub under an open-source license, enabling researchers to reproduce results and extend the benchmark with additional tasks or tools.

Future Impact

The benchmark offers a standardized, difficulty‑aware platform for evaluating LLM agents, which could inform future model development, safety assessments, and integration strategies across AI‑driven applications.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.