Benchmarking the agent in real-world tasks within a large-scale MCP toolset.
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## Dataset Description LiveMCPBench is the first comprehensive benchmark designed to evaluate LLM agents at scale across diverse Model Context Protocol (MCP) servers. It comprises 95 real-world tasks grounded in the MCP ecosystem, challenging agents to effectively use various tools in daily scenarios within complex, tool-rich, and dynamic environments. To support scalable and reproducible evaluation, LiveMCPBench is complemented by LiveMCPTool (a diverse collection of 70 MCP servers and 527 tools) and LiveMCPEval (an LLM-as-a-Judge framework that enables automated and adaptive evaluation). The benchmark offers a unified framework for benchmarking LLM agents in realistic, tool-rich, and dynamic MCP environments, laying a solid foundation for scalable and reproducible research on agent capabilities. ## Dataset Structure The dataset consists of `tasks.json`, which contains the 95 real-world tasks used for benchmarking LLM agents. ## Sample Usage You can load the dataset using the Hugging Face `datasets` library: ```python from datasets import load_dataset # Load the dataset dataset = load_dataset("ICIP/LiveMCPBench") # Print the dataset structure print(dataset) # Access an example from the test split print(dataset["test"][0]) ``` ## Citation If you find this project helpful, please use the following to cite it: ```bibtex @misc{mo2025livemcpbenchagentsnavigateocean, title={LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?}, author={Guozhao Mo and Wenliang Zhong and Jiawei Chen and Xuanang Chen and Yaojie Lu and Hongyu Lin and Ben He and Xianpei Han and Le Sun}, year={2025}, eprint={2508.01780}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2508.01780}, } ```