For many biologists, accessing The Cancer Genome Atlas (TCGA) data can be technically demanding. For example, a wet-lab scientist looking to retrieve gene expression data for breast cancer samples must locate metadata among files, then craft a manifest and execute commands—often resulting in errors, incomplete downloads, or wasted hours. TCGA is a pivotal resource for cancer genomics, yet its utility is limited for non-programmers due to reliance on command-line tools or complex scripting for data download. Although the NCI’s GDC Data Transfer Tool supports manifest-based downloads, it lacks a graphical interface and offers no interactive data exploration. This study aimed to introduce and evaluate TCGA-DL as a Windows-based graphical application for retrieval and organization of open-access TCGA data using GDC-compatible manifest files. The application provides graphical data selection combined with GDC-compatible, manifest-driven downloads from the GDC portal. Users can browse and filter datasets by cancer type or project, data category, and sample attributes via a point-and-click interface. For RNA-seq gene expression quantification files, the expanded benchmark used small, medium, and large GDC-compatible manifests (10, 100, and 300 files; approximately 42.34 MB, 423.39 MB, and 1.27 GB) with 10 repeated runs per tool. TCGA-DL completed the three datasets in 47.01 ± 3.80 s, 452.04 ± 5.57 s, and 1301.39 ± 66.73 s, respectively, showing lower mean completion times than the GDC Data Transfer Tool in this specific benchmark. TCGAbiolinks showed higher raw download speed in the medium and large datasets, but required installation of R, Bioconductor dependencies, and script-based execution, whereas TCGA-DL provided a Windows GUI workflow for users who do not work in R. TCGA-DL may help reduce usability barriers for biologists and clinical researchers who need access to TCGA data without extensive command-line interaction. It is a Windows-based tool (Python 3 and Tkinter) distributed under the MIT license.