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

arXiv: https://arxiv.org/abs/2510.01179 · HTML: https://mlsanity.com/paper/2510.01179/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use.
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Placed in

- LLM agents and tool use / [Tool learning and function calling](https://mlsanity.com/agents/tool-learning-and-function-calling/)

## Builds on (most cited first, 19 of 19)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environment](https://mlsanity.com/paper/2506.07982/) (2025)
- [ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases](https://mlsanity.com/paper/2306.05301/) (2023)
- [APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay](https://mlsanity.com/paper/2504.03601/) (2025)
- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding](https://mlsanity.com/paper/2503.02951/) (2025)
- [MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers](https://mlsanity.com/paper/2508.20453/) (2025)
- [MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers](https://mlsanity.com/paper/2508.14704/) (2025)
- [ACEBench: Who Wins the Match Point in Tool Usage?](https://mlsanity.com/paper/2501.12851/) (2025)
- [MCP-RADAR: A Multi-Dimensional Benchmark for Evaluating Tool Use Capabilities in Large Language Models](https://mlsanity.com/paper/2505.16700/) (2025)
- [API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs](https://mlsanity.com/paper/2402.15491/) (2024)
- [MCPWorld: A Unified Benchmarking Testbed for API, GUI, and Hybrid Computer Use Agents](https://mlsanity.com/paper/2506.07672/) (2025)
- [MCP-AgentBench: Evaluating Real-World Language Agent Performance with MCP-Mediated Tools](https://mlsanity.com/paper/2509.09734/) (2025)
- [LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries](https://mlsanity.com/paper/2508.15760/) (2025)
- [API Pack: A Massive Multi-Programming Language Dataset for API Call Generation](https://mlsanity.com/paper/2402.09615/) (2024)
- [A Scalable Approach to Clustering Embedding Projections](https://mlsanity.com/paper/2504.07285/) (2025)

## Built on by (most cited first, 30 of 69)

- [MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling](https://mlsanity.com/paper/2511.11793/) (2025)
- [Simulating Environments with Reasoning Models for Agent Training](https://mlsanity.com/paper/2511.01824/) (2025)
- [Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence](https://mlsanity.com/paper/2604.18292/) (2026)
- [Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning](https://mlsanity.com/paper/2604.12374/) (2026)
- [ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset](https://mlsanity.com/paper/2511.15718/) (2025)
- [LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls](https://mlsanity.com/paper/2511.09148/) (2025)
- [AutoForge: Automated Environment Synthesis for Agentic Reinforcement Learning](https://mlsanity.com/paper/2512.22857/) (2025)
- [EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL](https://mlsanity.com/paper/2605.18703/) (2026)
- [Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text](https://mlsanity.com/paper/2601.10355/) (2026)
- [Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models](https://mlsanity.com/paper/2512.24618/) (2025)
- [CacheWise: Understanding Workloads and Optimizing KVCache Management for Efficiently Serving LLM Coding Agents](https://mlsanity.com/paper/2606.16824/) (2026)
- [PhGPO: Pheromone-Guided Policy Optimization for Long-Horizon Tool Planning](https://mlsanity.com/paper/2602.13691/) (2026)
- [ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas](https://mlsanity.com/paper/2601.21558/) (2026)
- [LOGIGEN: Logic-Driven Generation of Verifiable Agentic Tasks](https://mlsanity.com/paper/2603.00540/) (2026)
- [Delegated Authorization for Agents Constrained to Semantic Task-to-Scope Matching](https://mlsanity.com/paper/2510.26702/) (2025)
- [EigenData: A Self-Evolving Multi-Agent Platform for Function-Calling Data Synthesis, Auditing, and Repair](https://mlsanity.com/paper/2603.05553/) (2026)
- [Controllable and Verifiable Tool-Use Data Synthesis for Agentic Reinforcement Learning](https://mlsanity.com/paper/2604.09813/) (2026)
- [Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use](https://mlsanity.com/paper/2602.20426/) (2026)
- [Tool-Genesis: A Task-Driven Tool Creation Benchmark for Self-Evolving Language Agent](https://mlsanity.com/paper/2603.05578/) (2026)
- [SING: Synthetic Intention Graph for Scalable Active Tool Discovery in LLM Agents](https://mlsanity.com/paper/2606.16591/) (2026)
- [NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness](https://mlsanity.com/paper/2609.08183/) (2026)
- [K2-V2: A 360-Open, Reasoning-Enhanced LLM](https://mlsanity.com/paper/2512.06201/) (2025)
- [Simulating Complex Multi-Turn Tool Calling Interactions in Stateless Execution Environments](https://mlsanity.com/paper/2601.19914/) (2026)
- [UniToolCall: Unifying Tool-Use Representation, Data, and Evaluation for LLM Agents](https://mlsanity.com/paper/2604.11557/) (2026)
- [ToolVerse: Unlocking Massive Environments and Long-Horizon Tasks for Agentic Reinforcement Learning](https://mlsanity.com/paper/2607.15660/) (2026)
- [ReCache: Efficient KV Cache Reuse and Compression for Tool-Augmented LLM Agents](https://mlsanity.com/paper/2608.19662/) (2026)
- [What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents](https://mlsanity.com/paper/2608.27260/) (2026)
- [Typhoon-S: Minimal Open Post-Training for Sovereign Large Language Models](https://mlsanity.com/paper/2601.18129/) (2026)
- [ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation](https://mlsanity.com/paper/2602.01709/) (2026)
- [Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs](https://mlsanity.com/paper/2605.17558/) (2026)

## Abstract (arXiv)

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.