Life science, data science, and mathematical modeling are so much more entertaining with the new AI tools available today. Here is a brief guide to what is out there and how you can use it to improve your science — from searching the literature to writing, analyzing, and visualizing your results.
AI models are evolving at a pace that is genuinely hard to keep up with. Every few weeks, a new model tops a benchmark, a new interface ships, and the previous “best option” quietly steps aside. More importantly, different models are now excelling at very different tasks, writing, coding, math, image generation, cost efficiency, which means that relying on a single model is increasingly a limitation, not a workflow.
To navigate this effectively, there are three things worth keeping distinct: the parent company, the model itself, and the interface you use to interact with it. These are three separate things that receive different names and are often conflated, which creates unnecessary confusion. A company like Anthropic builds models like Sonnet and Opus, which you can access through claude.ai, the desktop app, or Claude Code in the terminal. Same logic applies across the board. Getting this picture clear is the first step toward using AI deliberately rather than by habit.
AI can be an excellent tool to help scientists develop ideas, write papers, and refine their output. My impression is that resistance to adoption comes from a real limitation: research requires more than text editing. It requires connecting to the sources of knowledge. Since Anthropic popularized MCP servers, that connection has become practical. MCP (Model Context Protocol) is an open standard that lets AI models communicate directly with external tools and data sources. For scientists, this means your AI assistant can search the literature, retrieve papers, and rank them by citation impact, all within the same conversation. The best source for this is Scite, a paid service that not only counts citations but tells you whether a paper was supported or contradicted by subsequent work. Here I present the best free and low-cost alternatives, what each one is good for, and how to combine them depending on your workflow. The ranking below compares seven available MCP servers across citation quality, coverage, and workflow fit, with notes on what to pair with what. I have tried some of them and find them useful for different purposes.I’d genuinely like to hear what has worked for you.patricio@pperezh.com.
