With the increasing prevalence of fraudulent Android applications such as fake and malicious applications, it is crucial to detect them with high accuracy and adaptability. We present AgentDroid, a novel tool for Android fraudulent application detection based on multi-modal analysis and multi-agent systems. AgentDroid overcomes the limitations of traditional detection methods such as the inability to handle multimodal data and high false alarm rates. It processes Android applications and extracts a series of multi-modal data for analysis. Multiple LLM-based agents with specialized roles analyze the relevant data and collaborate to detect complex fraud effectively. We curated a dataset containing various categories of fraudulent applications and legitimate applications and validated our tool on this dataset. Experimental results indicate that our multi-agent tool based on GPT-4o achieves an accuracy of 91.7% and an F1-Score of 91.68%, outperforming the baseline methods. A video of AgentDroid is available at https://youtu.be/YOM9Ex-nBts.