ICIP/LiveMCPBench
LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools? Benchmarking the agent in real-world tasks within a large-scale MCP toolset. π Website | π Paper | π» Code | π Leaderboard | π Citation 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-worldβ¦ See the full description on the dataset page: https://huggingface.co/datasets/ICIP/LiveMCPBench.
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<!-- PROJECT --> <br /> <div align="center"> <h3 align="center">LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?</h3>
<p align="center"> Benchmarking the agent in real-world tasks within a large-scale MCP toolset. </p> </div>
<p align="center"> π <a href="https://icip-cas.github.io/LiveMCPBench" target="blank">Website</a> | π <a href="https://arxiv.org/abs/2508.01780" target="blank">Paper</a> | π» <a href="https://github.com/icip-cas/LiveMCPBench" target="blank">Code</a> | π <a href="https://docs.google.com/spreadsheets/d/1EXpgXq1VKw5A7l7-N2E9xt3w0eLJ2YPVPT-VrRxKZBw/edit?usp=sharing" target="blank">Leaderboard</a> | π <a href="#citation" target="_blank">Citation</a> </p>
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:
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:
@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},
}