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

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 pip install tooluniverse

• MCP Server: tooluniverse-smcp