TOUCAN: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP
  Environments
Authors: name: Zhangchen Xu, name: Adriana Meza Soria, name: Shawn Tan, name: Anurag Roy, name: Ashish Sunil Agrawal, name: Radha Poovendran, name: Rameswar Panda
Organization: IBM

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.