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

bash
pip install tooluniverse              # Standard
pip install tooluniverse[embedding]   # Embedding search (GPU)
pip install tooluniverse[all]         # All features

bash
export OPENAI_API_KEY="sk-..."  # Required for LLM tool search
export NCBI_API_KEY="..."       # Optional

Quick Start

python
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")

Core Patterns

Batch Execution

python
calls = [
    {"name": "UniProt_get_entry_by_accession", "arguments": {"accession": "P05067"}},
    {"name": "UniProt_get_entry_by_accession", "arguments": {"accession": "P12345"}},
]
results = tu.run_batch(calls)

Scientific Workflow

python
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()

Configuration

python
# 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"])

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

python
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', [])}")

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 for detailed guides.