From mims-harvard-tooluniverse
Build AI scientist systems using ToolUniverse Python SDK to access 1000+ scientific tools for drug discovery, protein/genomics analysis, literature research, and computational biology workflows.
npx claudepluginhub joshuarweaver/cascade-data-analytics --plugin mims-harvard-tooluniverseThis skill uses the workspace's default tool permissions.
**3 calling patterns -- start with pattern 1:**
Conducts multi-round deep research on GitHub repos via API and web searches, generating markdown reports with executive summaries, timelines, metrics, and Mermaid diagrams.
Dynamically discovers and combines enabled skills into cohesive, unexpected delightful experiences like interactive HTML or themed artifacts. Activates on 'surprise me', inspiration, or boredom cues.
Generates images from structured JSON prompts via Python script execution. Supports reference images and aspect ratios for characters, scenes, products, visuals.
3 calling patterns -- start with pattern 1:
tu.run({"name": ..., "arguments": ...}) -- single tool call, dict API (most portable)tu.tools.ToolName(param=value) -- function API (recommended for interactive use)pip install tooluniverse # Standard
pip install tooluniverse[embedding] # Embedding search (GPU)
pip install tooluniverse[all] # All features
export OPENAI_API_KEY="sk-..." # Required for LLM tool search
export NCBI_API_KEY="..." # Optional
from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools() # REQUIRED before any tool call
# Find tools
tools = tu.run({"name": "Tool_Finder_Keyword", "arguments": {"description": "protein structure", "limit": 10}})
# Execute (dict API)
result = tu.run({"name": "UniProt_get_entry_by_accession", "arguments": {"accession": "P05067"}})
# Execute (function API)
result = tu.tools.UniProt_get_entry_by_accession(accession="P05067")
calls = [
{"name": "UniProt_get_entry_by_accession", "arguments": {"accession": "P05067"}},
{"name": "UniProt_get_entry_by_accession", "arguments": {"accession": "P12345"}},
]
results = tu.run_batch(calls)
def drug_discovery_pipeline(disease_id):
tu = ToolUniverse(use_cache=True)
tu.load_tools()
try:
targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId(efoId=disease_id)
compound_calls = [
{"name": "ChEMBL_search_molecule_by_target",
"arguments": {"target_id": t['id'], "limit": 10}}
for t in targets['data'][:5]
]
compounds = tu.run_batch(compound_calls)
return {"targets": targets, "compounds": compounds}
finally:
tu.close()
# Caching
tu = ToolUniverse(use_cache=True)
stats = tu.get_cache_stats()
tu.clear_cache()
# Hooks (auto-summarization of large outputs)
tu = ToolUniverse(hooks_enabled=True)
# Load specific categories
tu.load_tools(categories=["proteins", "drugs"])
load_tools() before using any toolstools['tools'] after isinstance(tools, dict) checkUniProt_get_entry_by_accession not uniprot_get_...tu.all_tool_dict["ToolName"]['parameter'].get('required', [])from tooluniverse.exceptions import ToolError, ToolUnavailableError, ToolValidationError
try:
result = tu.tools.some_tool(param="value")
except ToolUnavailableError:
... # Tool service down
except ToolValidationError as e:
tool_info = tu.all_tool_dict["some_tool"]
print(f"Required: {tool_info['parameter'].get('required', [])}")
| 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 |