ToolUniverse

Overview

ToolUniverse is a unified ecosystem that enables AI agents to function as research scientists by providing standardized access to 600+ scientific resources. Use this skill to discover, execute, and compose scientific tools across multiple research domains including bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery.

Key Capabilities:

• Access 600+ scientific tools, models, datasets, and APIs
• Discover tools using natural language, semantic search, or keywords
• Execute tools through standardized AI-Tool Interaction Protocol
• Compose multi-step workflows for complex research problems
• Integration with Claude Desktop/Code via Model Context Protocol (MCP)

When to Use This Skill

Use this skill when:

• Searching for scientific tools by function or domain (e.g., "find protein structure prediction tools")
• Executing computational biology workflows (e.g., disease target identification, drug discovery, genomics analysis)
• Accessing scientific databases (OpenTargets, PubChem, UniProt, PDB, ChEMBL, KEGG, etc.)
• Composing multi-step research pipelines (e.g., target discovery → structure prediction → virtual screening)
• Working with bioinformatics, cheminformatics, or structural biology tasks
• Analyzing gene expression, protein sequences, molecular structures, or clinical data
• Performing literature searches, pathway enrichment, or variant annotation
• Building automated scientific research workflows

Quick Start

Basic Setup

from tooluniverse import ToolUniverse

# Initialize and load tools
tu = ToolUniverse()
tu.load_tools()  # Loads 600+ scientific tools

# Discover tools
tools = tu.run({
    "name": "Tool_Finder_Keyword",
    "arguments": {
        "description": "disease target associations",
        "limit": 10
    }
})

# Execute a tool
result = tu.run({
    "name": "OpenTargets_get_associated_targets_by_disease_efoId",
    "arguments": {"efoId": "EFO_0000537"}  # Hypertension
})

Model Context Protocol (MCP)

For Claude Desktop/Code integration:

tooluniverse-smcp

Core Workflows

1. Tool Discovery

Find relevant tools for your research task:

Three discovery methods:

• Tool_Finder - Embedding-based semantic search (requires GPU)
• Tool_Finder_LLM - LLM-based semantic search (no GPU required)
• Tool_Finder_Keyword - Fast keyword search

Example:

# Search by natural language description
tools = tu.run({
    "name": "Tool_Finder_LLM",
    "arguments": {
        "description": "Find tools for RNA sequencing differential expression analysis",
        "limit": 10
    }
})

# Review available tools
for tool in tools:
    print(f"{tool['name']}: {tool['description']}")

See references/tool-discovery.md for:

• Detailed discovery methods and search strategies
• Domain-specific keyword suggestions
• Best practices for finding tools

2. Tool Execution

Execute individual tools through the standardized interface:

Example:

# Execute disease-target lookup
targets = tu.run({
    "name": "OpenTargets_get_associated_targets_by_disease_efoId",
    "arguments": {"efoId": "EFO_0000616"}  # Breast cancer
})

# Get protein structure
structure = tu.run({
    "name": "AlphaFold_get_structure",
    "arguments": {"uniprot_id": "P12345"}
})

# Calculate molecular properties
properties = tu.run({
    "name": "RDKit_calculate_descriptors",
    "arguments": {"smiles": "CCO"}  # Ethanol
})

See references/tool-execution.md for:

• Real-world execution examples across domains
• Tool parameter handling and validation
• Result processing and error handling
• Best practices for production use

3. Tool Composition and Workflows

Compose multiple tools for complex research workflows:

Drug Discovery Example:

# 1. Find disease targets
targets = tu.run({
    "name": "OpenTargets_get_associated_targets_by_disease_efoId",
    "arguments": {"efoId": "EFO_0000616"}
})

# 2. Get protein structures
structures = []
for target in targets[:5]:
    structure = tu.run({
        "name": "AlphaFold_get_structure",
        "arguments": {"uniprot_id": target['uniprot_id']}
    })
    structures.append(structure)

# 3. Screen compounds
hits = []
for structure in structures:
    compounds = tu.run({
        "name": "ZINC_virtual_screening",
        "arguments": {
            "structure": structure,
            "library": "lead-like",
            "top_n": 100
        }
    })
    hits.extend(compounds)

# 4. Evaluate drug-likeness
drug_candidates = []
for compound in hits:
    props = tu.run({
        "name": "RDKit_calculate_drug_properties",
        "arguments": {"smiles": compound['smiles']}
    })
    if props['lipinski_pass']:
        drug_candidates.append(compound)

See references/tool-composition.md for:

• Complete workflow examples (drug discovery, genomics, clinical)
• Sequential and parallel tool composition patterns
• Output processing h

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