Motivation
The rapid advancement of technology has led to an exponential increase in the number of bioinformatics tools available for data analysis and biological discovery. However, keeping up with newly developed tools has become a major challenge, particularly for researchers without programming expertise. Tasks such as installing dependencies and executing analysis workflows often require proficiency in Unix command-line operations, resulting in a steep learning curve. Although graphical workflow platforms help lower these barriers, researchers must still learn how to operate these systems and understand their configuration and execution procedures. Furthermore, incorporating newly published tools into curated workflow ecosystems can be slow, delaying the adoption of the latest computational methods. To address these challenges, we developed AIBioAgentTools Repository, a repository of bioinformatics Model Context Protocol (MCP) servers that dynamically connect large language models with bioinformatics tools, enabling efficient execution of analyses on local computers.

Results
Using natural language commands, we demonstrate end-to-end automated workflow execution for Hi-C analysis as a case study, lowering the barrier to entry for complex multi-step analyses. AIBioAgentTools Repository is designed to support a diverse range of command-line tools across genomics, transcriptomics, and other omics disciplines, enabling scalable, reproducible, and AI-assisted bioinformatics analyses.

Co-authors: Melissa Jane Fullwood