TOUCAN: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP Environments

• URL: http://arxiv.org/abs/2510.01179v1
• Date: Wed, 01 Oct 2025 17:58:03 GMT
• Title: TOUCAN: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP Environments
• Authors: Zhangchen Xu, Adriana Meza Soria, Shawn Tan, Anurag Roy, Ashish Sunil Agrawal, Radha Poovendran, Rameswar Panda,
• Abstract summary: Toucan is the largest publicly available tool-agentic dataset to date.<n>It generates diverse, realistic, and challenging tasks with trajectories involving real tool execution.
• Score: 30.078263383249862
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Model (LLM) agents are rapidly emerging as powerful systems for automating tasks across domains. Yet progress in the open-source community is constrained by the lack of high quality permissively licensed tool-agentic training data. Existing datasets are often limited in diversity, realism, and complexity, particularly regarding multi-tool and multi-turn interactions. To address this gap, we introduce Toucan, the largest publicly available tool-agentic dataset to date, containing 1.5 million trajectories synthesized from nearly 500 real-world Model Context Protocols (MCPs). Unlike prior work, Toucan leverages authentic MCP environments to generate diverse, realistic, and challenging tasks with trajectories involving real tool execution. Our pipeline first produces a broad spectrum of tool-use queries using five distinct models, applies model-based quality filtering, and then generates agentic trajectories with three teacher models using two agentic frameworks. Rigorous rule-based and model-based validation ensures high-quality outputs. We also introduce three extension mechanisms to further diversify tasks and simulate multi-turn conversations. Models fine-tuned on Toucan outperform larger closed-source counterparts on the BFCL V3 benchmark and push the Pareto frontier forward on MCP-Universe Bench.

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