Agent-Ark

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 - Discover the methodology and technical details behind Toucan-1.5M - 💾 Github Repo - Access the complete pipeline used to produce Toucan-1.5M - 🤗 HF Dataset - Full dataset (You are here!) - 🤖 Model Checkpoints - Qwen2.5-7B | Qwen2.5-14B | Qwen2.5-32B This model is a fine-tuned variant of Qwen2.5-7B-Instruct, trained on a curated subset of the 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. 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. Contact: For questions, please contact Zhangchen by email.

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 - Discover the methodology and technical details behind Toucan-1.5M - 💾 Github Repo - Access the complete pipeline used to produce Toucan-1.5M - 🤗 HF Dataset - Full dataset (You are here!) - 🤖 Model Checkpoints - Qwen2.5-7B | Qwen2.5-14B | Qwen2.5-32B This model is a fine-tuned variant of Qwen2.5-14B-Instruct, trained on a curated subset of the 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. 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. Contact: For questions, please contact Zhangchen by email.