tooluniverse

name

tooluniverse

description

Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+ scientific tools including machine learning models, datasets, APIs, and analysis packages. Use when searching for scientific tools, executing computational biology workflows, composing multi-step research pipelines, accessing databases like OpenTargets/PubChem/UniProt/PDB/ChEMBL, performing tool discovery for research tasks, or integrating scientific computational resources into LLM workflows.

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 hooks
• Workflow best practices

Scientific Domains

ToolUniverse supports 600+ tools across major scientific domains:

Bioinformatics:

• Sequence analysis, alignment, BLAST
• Gene expression (RNA-seq, DESeq2)
• Pathway enrichment (KEGG, Reactome, GO)
• Variant annotation (VEP, ClinVar)

Cheminformatics:

• Molecular descriptors and fingerprints
• Drug discovery and virtual screening
• ADMET prediction and drug-likeness
• Chemical databases (PubChem, ChEMBL, ZINC)

Structural Biology:

• Protein structure prediction (AlphaFold)
• Structure retrieval (PDB)
• Binding site detection
• Protein-protein interactions

Proteomics:

• Mass spectrometry analysis
• Protein databases (UniProt, STRING)
• Post-translational modifications

Genomics:

• Genome assembly and annotation
• Copy number variation
• Clinical genomics workflows

Medical/Clinical:

• Disease databases (OpenTargets, OMIM)
• Clinical trials and FDA data
• Variant classification

See references/domains.md for:

• Complete domain categorization
• Tool examples by discipline
• Cross-domain applications
• Search strategies by domain

Reference Documentation

This skill includes comprehensive reference files that provide detailed information for specific aspects:

• references/installation.md - Installation, setup, MCP configuration, platform integration
• references/tool-discovery.md - Discovery methods, search strategies, listing tools
• references/tool-execution.md - Execution patterns, real-world examples, error handling
• references/tool-composition.md - Workflow composition, complex pipelines, parallel execution
• references/domains.md - Tool categorization by domain, use case examples
• references/api_reference.md - Python API documentation, hooks, protocols

Workflow: When helping with specific tasks, reference the appropriate file for detailed instructions. For example, if searching for tools, consult references/tool-discovery.md for search strategies.

Example Scripts

Two executable example scripts demonstrate common use cases:

scripts/example_tool_search.py - Demonstrates all three discovery methods:

• Keyword-based search
• LLM-based search
• Domain-specific searches
• Getting detailed tool information

scripts/example_workflow.py - Complete workflow examples:

• Drug discovery pipeline (disease → targets → structures → screening → candidates)
• Genomics analysis (expression data → differential analysis → pathways)

Run examples to understand typical usage patterns and workflow composition.

Best Practices

1. Tool Discovery:

   • Start with broad searches, then refine based on results
   • Use Tool_Finder_Keyword for fast searches with known terms
   • Use Tool_Finder_LLM for complex semantic queries
   • Set appropriate limit parameter (default: 10)

2. Tool Execution:

   • Always verify tool parameters before execution
   • Implement error handling for production workflows
   • Validate input data formats (SMILES, UniProt IDs, gene symbols)
   • Check result types and structures

3. Workflow Composition:

   • Test each step individually before composing full workflows
   • Implement checkpointing for long workflows
   • Consider rate limits for remote APIs
   • Use parallel execution when tools are independent

4. Integration:

   • Initialize ToolUniverse once and reuse the instance
   • Call load_tools() once at startup
   • Cache frequently used tool information
   • Enable logging for debugging

Key Terminology

• Tool: A scientific resource (model, dataset, API, package) accessible through ToolUniverse
• Tool Discovery: Finding relevant tools using search methods (Finder, LLM, Keyword)
• Tool Execution: Running a tool with specific arguments via tu.run()
• Tool Composition: Chaining multiple tools for multi-step workflows
• MCP: Model Context Protocol for integration with Claude Desktop/Code
• AI-Tool Interaction Protocol: Standardized interface for LLM-tool communication

Resources

• Official Website: https://aiscientist.tools
• GitHub: https://github.com/mims-harvard/ToolUniverse
• Documentation: https://zitniklab.hms.harvard.edu/ToolUniverse/
• Installation: uv pip install tooluniverse
• MCP Server: tooluniverse-smcp