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Rising clinical interest in phage therapy and the exponential growth of metagenomic sequence catalogues have driven a rapid expansion of bacteriophage bioinformatics. More than 80 dedicated tools, mostly published since 2020, now span identification, assembly, annotation, taxonomy, lifestyle prediction, defence-system detection and host prediction. Aimed at experienced practitioners and developers, this review synthesizes the field through the lens of three successive computational paradigms: sequence homology, bounded by database completeness; machine learning, constrained by labelled training data; and foundation models, which now achieve Matthews correlation coefficients above 0.95 for identification via structure-informed prediction. Furthermore, we map the upstream components, namely gene callers, homology engines, protein language models, and structural search tools, that underpin most downstream pipelines, exposing shared infrastructure and ecosystem-level fragility when dependencies change. To translate this into practice, we propose web-based and command-line reference workflows calibrated to user expertise and sample types. Finally, we set an agenda for the next wave of tool development. Roughly half of phage genes still resist functional annotation despite structural methods; no broadly generalisable strain-level host predictor exists for phage therapy; varying true positive rates (0% to 97%) underscore the absence of standardised community benchmarks analogous to CASP or CAMI. As generative genome models begin designing synthetic phages, progress will depend less on producing standalone tools than on rigorous evaluation, interoperable infrastructure, and clinically meaningful prediction targets.