Function-calling agent models, a significant advancement within large language models (LLMs), encounter challenges in requiring high-quality, diverse, and verifiable datasets. These models interpret natural language instructions to execute API calls crucial for real-time interactions with various digital services. However, existing datasets often lack comprehensive verification and diversity, resulting in inaccuracies and inefficiencies. Overcoming these challenges