tooluniverse-sdk — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited tooluniverse-sdk (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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 featuresexport OPENAI_API_KEY="sk-..." # Required for LLM tool search
export NCBI_API_KEY="..." # Optionalfrom 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"])tools['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 |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.