---
license: apache-2.0
datasets:
- Agent-Ark/Toucan-1.5M
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
tags:
- agent
---

# 🦤 Toucan-1.5M: 

Toucan-1.5M is the largest fully synthetic tool-agent dataset to date, designed to advance tool use in agentic LLMs. It comprises over 1.5 million trajectories synthesized from 495 real-world Model Context Protocols (MCPs) spanning 2,000+ tools. By leveraging authentic MCP environments, Toucan-1.5M generates diverse, realistic, and challenging tasks requires using multiple tools, with trajectories involving real tool executions across multi-round, multi-turn, sequential, and parallel tool calls. Models fine-tuned on Toucan-1.5M outperform much larger closed-source counterparts on the BFCL V3 benchmark and extend the Pareto frontier on the MCP-Universe benchmark.

- 📄 [Technical Report](https://arxiv.org/abs/2510.01179) - Discover the methodology and technical details behind Toucan-1.5M
- 💾 [Github Repo](https://github.com/TheAgentArk/Toucan) - Access the complete pipeline used to produce Toucan-1.5M
- 🤗 [HF Dataset](https://huggingface.co/datasets/Agent-Ark/Toucan-1.5M) - Full dataset (You are here!)
- 🤖 Model Checkpoints - [Qwen2.5-7B](https://huggingface.co/Agent-Ark/Toucan-Qwen2.5-7B-Instruct-v0.1) | [Qwen2.5-14B](https://huggingface.co/Agent-Ark/Toucan-Qwen2.5-7B-Instruct-v0.1) | [Qwen2.5-32B](https://huggingface.co/Agent-Ark/Toucan-Qwen2.5-32B-Instruct-v0.1)

![Toucan-Pipeline](https://cdn-uploads.huggingface.co/production/uploads/653df1323479e9ebbe3eb6cc/Dcz-NP1tfcJriku8FP2OT.jpeg)

## About This Model

This model is a fine-tuned variant of **Qwen2.5-7B-Instruct**, trained on a curated subset of the [Toucan-1.5M](https://huggingface.co/datasets/Agent-Ark/Toucan-1.5M) dataset. The supervised fine-tuning (SFT) subset consists of **119.3K instances** in total, including:

- **28.3K** from the original pipeline  
- **40K** from Extension 1 (*Irrelevance*)  
- **15.8K** from Extension 2 (*Diversify*)  
- **35.2K** from Extension 3 (*Multi-Turn*)  

We adopt the `Hermes` prompt template for fine-tuning. For a detailed description of the training setup and hyperparameters, please refer to our [technical report](https://arxiv.org/abs/2510.01179).

## Model Performance

Toucan-1.5M remarkably improves baseline model performance through SFT and enables smaller models to outperform larger models across different evaluation aspects, as evidenced in BFCL-V3 and MCP Universe benchmarks.
 
![hf_bench_perf](https://cdn-uploads.huggingface.co/production/uploads/653df1323479e9ebbe3eb6cc/dxJvZO1es6AkMF9PdUS-k.jpeg)

## 📚 Citation

If you find the data or code useful, please cite:
```
@misc{xu2025toucan,
      title={TOUCAN: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP Environments}, 
      author={Zhangchen Xu and Adriana Meza Soria and Shawn Tan and Anurag Roy and Ashish Sunil Agrawal and Radha Poovendran and Rameswar Panda},
      year={2025},
      eprint={2510.01179},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2510.01179}, 
}
```

**Contact**: For questions, please contact [Zhangchen](mailto:zxu9@uw.edu) by email.