This paper emphasizes an exploration based on surveys on how the different large language models (LLM) respond and perform when they are integrated with the API to automate email administration. The study investigates the ability of each model to understand the indications of natural language, generate consistent responses and execute tasks such as composing, sending and reading emails. Through the tool calling capabilities, these models can directly invoke API functions, allowing the execution of perfect tasks through natural language commands. The call of tools serves as a critical mechanism that allows the LLM to go beyond static responses and interact dynamically with external systems, effectively transforming them into active agents capable of completing user -intended tasks. This paper explores how each model interprets the signatures of the function, select the appropriate tools and handle the interactions of several steps using these capabilities. With this exploration, the article highlights the differences in the capacity of response of the model, precision and contextual understanding in cases of use of the real world. Provides information on the strengths and limitations of each LLM in real world applications. This study additionally analyzes how LLM's behavior varies between models in terms of reliability, latency and context retention