ToolUniverse is a collection of tools, interfaces, and supporting components for building AI systems that perform scientific work. It is for developers creating AI scientist agents that use APIs, databases, machine-learning tools, and domain-specific utilities. The catalogue includes skills, commands, an MCP server, an agent, and a hook for working with the ecosystem.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-binder-discoverygit clone --depth 1 https://github.com/mims-harvard/ToolUniverseWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-binder-discovery)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-binder-discovery"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-binder-discovery/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-binder-discovery"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-binder-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00080 | $0.03637 |
| Opus 5 | $0.00040 | $0.01818 |
| Sonnet 5 | $0.00016 | $0.00727 |
| Haiku 4.5 | $0.00008 | $0.00364 |
Grade A, and why
tooluniverse-binder-discovery scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
props = pd.DataFrame(requests.get(url).json()["PropertyTable"]["Properties"]) How it starts
The opening of the file, as written. The whole thing — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Small Molecule Binder Discovery Strategy
Systematic discovery of novel small molecule binders using 60+ ToolUniverse tools across druggability assessment, known ligand mining, similarity expansion, ADMET filtering, and synthesis feasibility.
LOOK UP DON'T GUESS - Always retrieve actual data from tools before drawing conclusions. Do not assume druggability, binding sites, or compound properties based on target class alone.
KEY PRINCIPLES:
- Report-first approach - Create report file FIRST, then populate progressively
- Target validation FIRST - Confirm druggability before compound searching
- Multi-strategy approach - Combine structure-based and ligand-based methods
- ADMET-aware filtering - Eliminate poor compounds early
- Evidence grading - Grade candidates by supporting evidence
- Actionable output - Provide prioritized candidates with rationale
- English-first queries - Always use English terms in tool calls. Respond in the user's language
Binding Site Reasoning (Start Here)
Before any tool call, reason about the target's structural biology:
Is the binding site a well-defined pocket (small molecule accessible) or a flat protein-protein interface (needs peptide/macrocycle)? This determines your screening strategy.
- Enzymes with active sites (proteases, kinases, ATPases): deep, well-defined pockets. Classic small molecule territory. Prioritize co-crystal structure search and known inhibitor scaffold analysis.
- GPCRs and ion channels: transmembrane pockets. Structure often available; start with GPCRdb and GtoPdb for known pharmacology.
- Nuclear receptors: deep hydrophobic pockets. Excellent small molecule tractability; ligand-based methods are well-powered.
- Protein-protein interfaces: flat, large contact surface. Small molecules rarely compete effectively unless there is a "hot spot" cavity. Check whether any allosteric pockets exist before committing to small molecule strategy. Warn the user if no pocket is found.
- Intrinsically disordered regions: essentially no small molecule approach. Redirect to peptide or degrader strategies.
- Scaffolding / adaptor proteins: assess co-crystal structures for unexpected pockets before declaring undruggable.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 298 lines · 80 tokens per session scan A ea2f88bafa5c
tooluniverse-binder-discovery is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,680 stars, last pushed 2d ago), licensed Apache-2.0. It adds 80 tokens to every session and 3,637 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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