TOUCAN: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP
  Environments
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.
68de23376024653e8a3ed1e2
Zhangchen Xu
claimed_verified
2025-10-03T12:54:22.052Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/653df1323479e9ebbe3eb6cc/K_g-r1iMRNKj99LXPuYF3.jpeg
Zhangchen Xu
user
zhangchenxu
68de23376024653e8a3ed1e3
Adriana Meza Soria
68de23376024653e8a3ed1e4
Shawn Tan
68de23376024653e8a3ed1e5
Anurag Roy
68de23376024653e8a3ed1e6
Ashish Sunil Agrawal
claimed_verified
2025-10-05T12:47:31.770Z
639582473d9ac9664fd436f5
https://cdn-avatars.huggingface.co/v1/production/uploads/639582473d9ac9664fd436f5/yWH354yTLKVchFD2N498z.jpeg
Ashish Sunil Agrawal
user
ashish23
68de23376024653e8a3ed1e7
Radha Poovendran
68de23376024653e8a3ed1e8
Rameswar Panda
TOUCAN: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP
  Environments
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.
Toucan, a large publicly available tool-agentic dataset, enhances the performance of LLM agents by providing diverse, realistic, and complex multi-tool and multi-turn interactions.
Large Language Model (LLM) tool-agentic training data Model Context Protocols (MCPs) tool-use queries model-based quality filtering agentic trajectories teacher models agentic frameworks BFCL V3 benchmark MCP-Universe Bench