Build AI scientist systems with the ToolUniverse Python SDK for scientific research. Covers the 3 calling patterns (`tu.run` portable dict API, `tu.tools.X` function API, direct class instantiation), tool loading, batch execution, MCP server integration, and embedding-based tool search. Use for SDK

ToolUniverse Python SDK 3 calling patterns start with pattern 1: 1. tu.run({"name": ..., "arguments": ...}) single tool call, dict API (most portable) 2. tu.tools.ToolName(param=value) function API (recommended for interactive use) 3. Direct class instantiation advanced, bypasses caching/hooks Installation Quick Start Core Patterns Batch Execution Scientific Workflow Configuration Critical Notes 1. Always call load tools() before using any tools 2. Tool Finder returns nested structure : access via tools['tools'] after isinstance(tools, dict) check 3. Tool names are case sensitive : UniProt get entry by accession not uniprot get ... 4. Check required params : tu.all tool dict["ToolName"]['parameter'].get('required', []) 5. Cache deterministic calls (ML predictions, DB queries); don't cache real time data Error Handling Tool Categories Category Tools Use Cases Proteins UniProt, RCSB PDB, AlphaFold Protein analysis, structure Drugs DrugBank, ChEMBL, PubChem Drug discovery, compounds Genomics Ensembl, NCBI Gene, gnomAD Gene analysis, variants Diseases OpenTargets, ClinVar Disease target associations Literature PubMed, Europe PMC Literature search ML Models ADMET AI, AlphaFold Predictions, modeling Pathways KEGG, Reactome Pathway analysis Resources Docs : https://zitniklab.hms.harvard.edu/ToolUniverse/ GitHub : https://github.com/mims harvard/ToolUniverse See [REFERENCE.md](REFERENCE.md) for detailed guides.

tooluniverse-sdk