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-biomedical-fact-lookupgit 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-biomedical-fact-lookup)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-biomedical-fact-lookup"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-biomedical-fact-lookup/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-biomedical-fact-lookup"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-biomedical-fact-lookup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00184 | $0.04231 |
| Opus 5 | $0.00092 | $0.02116 |
| Sonnet 5 | $0.00037 | $0.00846 |
| Haiku 4.5 | $0.00018 | $0.00423 |
Grade A, and why
tooluniverse-biomedical-fact-lookup scanned grade A with 0 findings 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 13d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Biomedical Fact Lookup (tool-grounded answering)
Factual biomedical questions — "which gene is in set X", "which gene is associated with disease Y according to DisGeNet", "which gene has a TF binding site per GTRD" — have an authoritative answer in a public database. Guessing from memory is unreliable (≈chance on niche annotations); the matching ToolUniverse tool returns the ground truth.
RULE ZERO: Look it up, never guess
If a question names a database, a gene set, or any annotation that lives in a database, you MUST query the tool before answering. Answering a "according to " question from memory is a failure mode — these annotations (predicted miRNA targets, ChIP-seq binding, curated gene sets, disease associations) are exactly what models hallucinate. A tool-verified answer beats any recalled fact.
Multiple-choice procedure
Most of these questions are MCQ with an "Insufficient information to answer the question." distractor. Do this:
- Parse the question for: the named database/collection, the anchor entity (the gene set, disease, miRNA, TF, locus…), and the candidate options.
- Resolve the anchor to the right tool + identifier (see Routing table).
- Query the tool once to get the authoritative member list / association set.
- Check each option against that result. Exactly one option should be supported.
- Answer with that option's letter. Only choose "Insufficient information" if the tool genuinely returns nothing for a valid query (not because you skipped the query).
Routing table — question pattern → tool
| Question mentions… | Tool(s) (verified) | How |
|---|---|---|
a named gene set / oncogenic signature (MSigDB C6, e.g. ATM_DN.V1_DN) |
MSigDB_get_gene_set_members |
list members, check which option is in it |
| miRNA target "according to miRDB" (e.g. MIR186-3p) | MSigDB_get_gene_set_members (collection C3:MIR:MIRDB) |
set name = MIR<number>_<3P|5P>, e.g. MIR186_3P |
| TF binding site / target "according to GTRD" (e.g. PGM3) | MSigDB_check_gene_in_set (collection C3:TFT:GTRD) |
set name = <TF>_TARGET_GENES, e.g. PGM3_TARGET_GENES; pass gene per option |
| pathway / hallmark membership | MSigDB_get_hallmark_geneset, MSigDB_get_geneset |
HALLMARK_<NAME> or exact set name |
| gene ↔ disease association (DisGeNet, OpenTargets, OMIM) | umls_search_concepts → DisGeNET_get_disease_genes/DisGeNET_get_gda; OpenTargets_*, MyDisease_get_disease, OMIM_search; text-mined fallback: PubTator3_LiteratureSearch / PubTator3_GetEntityRelations (e1=@GENE_<sym>), EPMC_get_text_mined_annotations |
DisGeNET needs a UMLS CUI (resolve via umls_search_concepts → C0152200, then disease=C0152200) + DISGENET_API_KEY. See the "in X but not Y" recipe below |
| mouse phenotype gene set (MGI / MP:xxxxx, e.g. "increased carcinoma incidence") | MGI_search_genes → MGI_get_phenotypes |
for each candidate gene: search → take the MGI: id → MGI_get_phenotypes; the matching gene is the one whose phenotype_statement list contains the phenotype the question names (see interpretation note) |
| gene genomic location (Ensembl band, e.g. chr7q34) | Ensembl_* / NCBIDatasets_get_gene_by_symbol |
resolve each option, compare cytoband/coordinates |
| variant / sequence pathogenicity ("which variant/sequence is pathogenic or benign per ClinVar") | (only when genuinely unsure) annotate_variant_multi_source, VEP_predict_pathogenicity, UniProt_get_disease_variants_by_accession |
Be efficient — do NOT query every option (that causes timeouts). Identify the protein once, find each option's single substitution, and reason about the specific residue changes directly; the base model is usually reliable on well-characterized ClinVar variants. Make at most ONE targeted tool call to resolve a truly uncertain variant. Watch the question's polarity (benign vs pathogenic): for "most likely benign", a common/reference-matching variant is the answer; for "most likely pathogenic", a rare damaging one is. |
| drug / compound target, MoA, approval | ChEMBL_*, OpenFDA_*, GtoPdb_*, PubChem_* |
resolve drug, query the relation |
| protein function / domain / sequence | UniProt_* |
resolve accession, read annotation |
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.
- 13d ago First seen · 164 lines · 184 tokens per session scan A b0a13cc2e437
tooluniverse-biomedical-fact-lookup is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,680 stars, last pushed 3d ago), licensed Apache-2.0. It adds 184 tokens to every session and 4,231 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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