Why LLMs Need Dedicated Tools, Not Guesswork, for Medical Scheduling

Large language models like Claude and GPT-4 struggle with deterministic tasks such as medication scheduling, often producing incorrect dates when constraints like preferred days of the week are introduced. Because LLMs are probabilistic by nature, errors in dose calculations carry serious liability risks in healthcare settings. To address this, the Injection Day Alignment MCP server provides three structured tools — schedule generation, shift-offset calculation, and compliance verification — that handle the underlying arithmetic reliably. These primitives use ISO 8601 date formats to eliminate timezone ambiguity and support complex constraints such as aligning injection days to avoid pharmacy closures. The broader argument is that AI agents operating in sensitive domains require precise, deterministic interfaces rather than relying on models to perform critical logic internally.