tooluniverse

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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.

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Sourceovachiever/droid-tings

Added on2026-02-07

NPX Install

npx skill4agent add ovachiever/droid-tings tooluniverse

SKILL.md Content

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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

python

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:

bash

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:

python

# 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:

python

# 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:

python

# 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

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)
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
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
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 uv pip install tooluniverse
```
MCP Server:
```
tooluniverse-smcp
```

tooluniverse

NPX Install

Tags

SKILL.md Content

ToolUniverse

Overview

When to Use This Skill

Quick Start

Basic Setup

Model Context Protocol (MCP)

Core Workflows

1. Tool Discovery

2. Tool Execution

3. Tool Composition and Workflows

Scientific Domains

Reference Documentation

Example Scripts

Best Practices

Key Terminology

Resources