Content
## ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox
**ComplexMCP** is a benchmark for evaluating model performance in complex software workflows and large API tool ecosystems.
<p align="center">
<a href="https://arxiv.org/abs/2605.10787" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2605.10787v1-b31b1b?logo=arxiv&logoColor=white&style=for-the-badge" alt="arXiv badge">
</a>
</p>
### 1) Build Environment Via Docker
```bash
docker build -t complexmcp:latest .
```
```bash
docker run -d --name complexmcp \
-p 8000-8007:8000-8007 \
-p 9000-9006:9000-9006 \
complexmcp:latest
```
### 2) Create `.env`
Create a `.env` file in the project root, following `.env.example` format.
```bash
cp .env.example .env
```
Then fill values in `.env` as needed.
### 3) Run Benchmark
```bash
python run_benchmark.py --tool-config config/general.yaml \
--model [model_name]
```
If you find this work helpful, please cite our paper:
```latex
@misc{li2026complexmcpevaluationllmagents,
title={ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox},
author={Yuanyang Li and Xue Yang and Longyue Wang and Weihua Luo and Hongyang Chen},
year={2026},
eprint={2605.10787},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2605.10787},
}
```
MCP Store