MCPInstall

tooluniverse-sdk

by mims-harvardBackend & APIs

Build AI scientist systems with the ToolUniverse Python SDK for scientific research. Covers the 3 calling patterns (tu.run portable dict API, tu.tools.X function API, direct class instantiation), tool loading, batch execution, MCP server integration, and embedding-based tool search. Use for SDK programming, custom tool composition, benchmarking pipelines, and integrating ToolUniverse into research workflows.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-sdk
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads tooluniverse-sdk automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

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

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

Quick Start

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

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

def drug_discovery_pipeline(disease_id):
    tu = ToolUniverse()
    tu.load_tools()
    try:
        targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId(efoId=disease_id)
        # ChEMBL takes ChEMBL target IDs, not the Ensembl IDs in `targets`;
        # map them first (e.g. via ChEMBL_search_targets). Example IDs: EGFR, COX-2
        chembl_target_ids = ["CHEMBL203", "CHEMBL230"]
        compound_calls = [
            {"name": "ChEMBL_search_activities",
             "arguments": {"target_chembl_id": tid, "limit": 10}}
            for tid in chembl_target_ids
        ]
        compounds = tu.run_batch(compound_calls)
        return {"targets": targets, "compounds": compounds}
    finally:
        tu.close()

Configuration

# Caching: opt in per call (use_cache is a framework option on tools.X(...),
# not an argument to ToolUniverse() or to the tool itself)
tu = ToolUniverse()
result = tu.tools.some_tool(param="value", 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

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

CategoryToolsUse Cases
ProteinsUniProt, RCSB PDB, AlphaFoldProtein analysis, structure
DrugsDrugBank, ChEMBL, PubChemDrug discovery, compounds
GenomicsEnsembl, NCBI Gene, gnomADGene analysis, variants
DiseasesOpenTargets, ClinVarDisease-target associations
LiteraturePubMed, Europe PMCLiterature search
ML ModelsADMET-AI, AlphaFoldPredictions, modeling
PathwaysKEGG, ReactomePathway analysis

Resources

FAQ

What does the tooluniverse-sdk skill do?

Build AI scientist systems with the ToolUniverse Python SDK for scientific research. Covers the 3 calling patterns (tu.run portable dict API, tu.tools.X function API, direct class instantiation), tool loading, batch execution, MCP server integration, and embedding-based tool search. Use for SDK programming, custom tool composition, benchmarking pipelines, and integrating ToolUniverse into research workflows.

How do I install tooluniverse-sdk?

Run npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-sdk in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tooluniverse-sdk/ (personal) or .claude/skills/tooluniverse-sdk/ (project).

Which AI agents can use tooluniverse-sdk?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is tooluniverse-sdk safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains tooluniverse-sdk?

It is published by mims-harvard in mims-harvard/tooluniverse. It has 398 recorded installs.

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