You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions.
system
[{"name": "polygon_area_shoelace", "description": "Calculates the area of a polygon using the shoelace formula.", "parameters": {"vertices": {"description": "A list of polygon vertices represented as tuples (x, y).", "type": "List[Tuple[float, float]]"}}}, {"name": "find_n_largest_numbers", "description": "Finds the n largest numbers in a list.", "parameters": {"nums": {"description": "The list of numbers.", "type": "List[int]"}, "n": {"description": "The number of largest numbers to find.", "type": "int"}}}, {"name": "merge_sorted_lists", "description": "Merges two sorted lists into a single sorted list.", "parameters": {"list1": {"description": "The first sorted list.", "type": "List[int]"}, "list2": {"description": "The second sorted list.", "type": "List[int]"}}}, {"name": "binomial_probability", "description": "Calculates the probability of getting exactly k successes in n independent trials,", "parameters": {"n": {"description": "The total number of trials.", "type": "int"}, "k": {"description": "The number of successes.", "type": "int"}, "p": {"description": "The probability of success in each trial.", "type": "float"}}}]
Could you assist with some calculations? First, find the area of a polygon with vertices at (0,0), (1,6), (7,9), and (10,3). Next, identify the four largest numbers from the list [20, 12, 30, 25, 15, 28]. Merge these two sorted lists: [2, 5, 8] and [3, 6, 9]. Lastly, calculate the probability of 4 successes in 12 trials with a 0.6 success rate.
user
assistant
[{"name": "polygon_area_shoelace", "arguments": {"vertices": [[0, 0], [1, 6], [7, 9], [10, 3]]}}, {"name": "find_n_largest_numbers", "arguments": {"nums": [20, 12, 30, 25, 15, 28], "n": 4}}, {"name": "merge_sorted_lists", "arguments": {"list1": [2, 5, 8], "list2": [3, 6, 9]}}, {"name": "binomial_probability", "arguments": {"n": 12, "k": 4, "p": 0.6}}]