A Python SDK for building AI research systems that need to orchestrate scientific tools across proteins, drugs, genomics, and literature databases. You get three calling patterns (portable dict API, function API, or direct instantiation), built-in caching for deterministic calls, and batch execution for parallel workflows. The tool discovery is embedding-based, which is helpful when you're not sure which of the 100+ tools you actually need. Main gotcha is remembering to call load_tools() before anything else, and tool names are case-sensitive. Best for building reproducible research pipelines or benchmarking systems where you need programmatic access to UniProt, ChEMBL, PubMed, AlphaFold predictions, and similar scientific APIs without writing individual integrations yourself.
npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-sdk