Testing tool-calling accuracy in an agent involves systematically evaluating its ability to correctly identify and execute external functions based on user prompts. A primary method is to create a diverse dataset of scenarios, each with meticulously defined ground truth tool calls, specifying the exact function name and arguments expected. The agent is then run against these predefined test cases, and its predicted tool calls (including function name, argument structure, and values) are programmatically compared against the established ground truth. Metrics such as precision, recall, and F1-score are commonly employed to quantify accuracy, particularly when agents might call multiple tools or omit necessary ones. While automated comparison of JSON outputs for argument matching is highly effective, human review for nuanced cases or complex conditional logic remains essential to catch subtle errors. This iterative testing process not only identifies failures but also helps refine the agent's tool invocation logic and prompt engineering strategies for improved reliability. More details: https://www.library.statecouncil.gov.eg/Site/Template/Set_language.aspx?Lang=A&URl=https://infoguide.com.ua/