toucan-mcp-tool-agentic-dataset

name	toucan-mcp-tool-agentic-dataset
title	TOUCAN: Synthesizing Tool-Agentic Data from MCP Environments
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2510.01179
keywords	["MCP","agent-training","dataset-synthesis","tool-use","LLM-agents"]
description	Generate 1.5M realistic agent training trajectories from 495 real-world MCP servers without human annotation. Use to build tool-agentic training datasets with authentic tool execution, multi-turn conversations, and error handling.

TOUCAN: Synthesizing Tool-Agentic Data from MCP Environments

TOUCAN introduces the largest open-source tool-agentic dataset with 1.5M trajectories generated from real-world MCP (Model Context Protocol) servers. By leveraging authentic MCP execution rather than simulation, the dataset enables LLM agent training with realistic tool behavior, error patterns, and complex workflows.

Core Architecture

• Real MCP execution: 495 servers with actual tool behavior (no simulation)
• 1.5M trajectories: Multi-turn conversations with parallel tool calls
• Quality filtering: Six-dimension validation (relevance, correctness, completeness, diversity, executability, safety)
• Diverse interactions: Error recovery, irrelevant queries, multi-step reasoning
• State-of-the-art results: Fine-tuned 32B models outperform GPT-4.5-Preview on multi-turn benchmarks

Implementation Steps

Setup MCP server discovery and trajectory synthesis:

# Initialize TOUCAN dataset generation pipeline
from toucan import MCPDatasetSynthesizer, QualityFilter

synthesizer = MCPDatasetSynthesizer(
    num_servers=495,
    trajectory_budget=1_500_000,
    parallel_workers=128,
    server_timeout=30
)

# Discover available MCP servers
servers = synthesizer.discover_servers(
    exclude_api_key_dependent=True,  # avoid credential exposure
    max_per_category=50
)

# Configure quality filtering (6-dimension validation)
quality_filter = QualityFilter(
    dimensions={
        "relevance": 0.85,
        "correctness": 0.90,
        "completeness": 0.80,
        "diversity": 0.75,
        "executability": 0.95,
        "safety": 1.0
    }
)

Generate trajectories with error handling and quality assurance: