Since the integration of AI coding assistants into scientific research workflows has outpaced the development of standardised practices for their use, a gap has emerged between what these tools are capable of and what bioinformatics researchers can reliably extract from them. In order to address this gap, this essay argues for the systematic adoption of AI context configuration files, structured plain-text documents deposited alongside open-source bioinformatics tools that supply an AI assistant with the domain knowledge, operational constraints, and behavioural guidelines necessary to interact correctly with a specific pipeline. The argument proceeds on two levels: that such files substantially reduce the technical and financial barriers to bioinformatics tool usage for non-specialist researchers, and that they improve the reproducibility and analytical consistency of AI-assisted analyses. A two-tier community governance model is proposed, in which the original tool developers, or verified domain experts in their absence, serve as the authoritative authors of context files distributed with each versioned software release. A reference table of 13 peer-reviewed bioinformatics tools across seven fields is included to illustrate the practical scope of the proposal.