TOUCAN: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP Environments
This addresses a data bottleneck for the open-source community in developing LLM agents, though it is incremental as it builds on existing tool-agentic frameworks.
The authors tackled the lack of high-quality, permissively licensed tool-agentic training data for LLM agents by introducing Toucan, a dataset of 1.5 million trajectories synthesized from real-world MCP environments, which led to models fine-tuned on it outperforming larger closed-source counterparts on benchmarks like BFCL V3 and MCP-Universe Bench.
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.