embl-ebi-ols
google-deepmind/science-skills
Search 250+ biomedical ontologies (GO, DOID, HP, CHEBI, UBERON) for terms, definitions, and hierarchies.
What is embl-ebi-ols?
Query the EMBL-EBI Ontology Lookup Service to find biomedical ontology terms, retrieve definitions, navigate parent-child relationships, and explore term hierarchies. Use this when you need to look up disease codes, gene functions, phenotypes, chemical entities, or anatomical structures across standardized biomedical vocabularies.
- Search across 250+ ontologies by keyword with exact matching and pagination support
- Retrieve full term details including definitions, synonyms, and ontology metadata
- Navigate term hierarchies: parents, children, ancestors, descendants, and hierarchical relations
- Get autocomplete suggestions for partial term names
- Fetch ontology-wide statistics and root terms
- Filter results by ontology, entity type, or defining ontology only
How to install embl-ebi-ols
npx skills add https://github.com/google-deepmind/science-skills --skill embl-ebi-ols- uv must be installed and on PATH (see uv skill)
- User must acknowledge EMBL-EBI OLS terms at https://www.ebi.ac.uk/ols4/api-docs before first use
How to use embl-ebi-ols
- 1.Search for a term using search_ols.py with --query and optionally --ontology to narrow results
- 2.Use --exact flag when you need precise entity resolution to a single term ID
- 3.Extract the OBO ID from search results
- 4.Call get_term.py with the OBO ID and --relations flag to explore parents, children, or ancestors
- 5.Use suggest_ols.py for autocomplete on partial term names
- 6.Paginate large result sets with --rows and --start parameters
Use cases
- Find the OBO ID and definition for a disease like myocardial infarction in DOID
- Look up parent diseases or disease categories for a specific condition
- Retrieve all phenotypes (HP terms) associated with a genetic disorder
- Search for a gene function in GO and explore its parent functions
- Find anatomical structures in UBERON and their part-of relationships
- Biomedical researchers and bioinformaticians
- Clinical informaticists mapping disease or phenotype codes
- Biocurators working with standardized ontologies
- Data scientists building knowledge graphs from biomedical data
- Anyone needing to resolve biomedical terminology to standard identifiers
embl-ebi-ols FAQ
Use DOID for common human diseases (diabetes, cancer, myocardial infarction). Use MONDO only when cross-species context is explicitly needed. Use HP for phenotypes, GO for gene functions, CHEBI for chemicals, UBERON for anatomy, and CL for cell types.
First search for the disease term to get its OBO ID (e.g., search_ols.py --query 'myocardial infarction' --ontology doid --exact). Then call get_term.py with that OBO ID and --relations parents to retrieve parent terms.
Parents uses only is-a relationships, while hierarchicalParents includes is-a plus transitive relations like part-of and develops-from. Use hierarchical variants for anatomical and developmental ontologies.
Yes, omit the --ontology flag to search across all 250+ ontologies, but results will be noisier. It is recommended to specify --ontology when you know which ontology to search.
Respect a maximum of 5 requests per second per EMBL-EBI Terms of Use. The provided utility scripts automatically enforce this rate limiting.
Full instructions (SKILL.md)
Source of truth, from google-deepmind/science-skills.
name: embl-ebi-ols description: > Query and search the EMBL-EBI Ontology Lookup Service (OLS) for biomedical ontology terms, definitions, and hierarchies across 250+ ontologies (e.g., GO, DOID, HP). Use when the user asks to search for terms, retrieve details, navigate hierarchies (parents, children, ancestors), look up properties and individuals, get autocomplete suggestions, or access ontology metadata and statistics.
EMBL-EBI Ontology Lookup Service (OLS)
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If .licenses/embl_ebi_ols_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://www.ebi.ac.uk/ols4/api-docs, then (2) create the file recording the notification text and timestamp.
Core Rules
-
[!IMPORTANT] Use the Utility Scripts: You MUST ALWAYS use the provided utility script under
scripts/for all API interactions, including checking status. NEVER usecurlor custom Python requests to query API directly. -
Rate Limiting & Resilience: You MUST respect EBI's Terms of Use with a maximum 5 requests per second. The provided utility scripts automatically enforce this.
-
Notification: If this skill is used, ensure this is mentioned in the output.
When to Use — Quick Recipes
Use this skill whenever a user query matches one of these patterns:
- Definition of a disease, phenotype, or term →
get_term.py --obo_id <ID> --summary - Subtypes or children of a term →
get_term.py --obo_id <ID> --relations children - Parent of a term →
get_term.py --obo_id <ID> --relations parents - Ancestors / disease categories / classified under →
get_term.py --obo_id <ID> --relations ancestors - Root terms of an ontology →
get_term.py --ontology <id> --roots - Hierarchical parents (is-a + part-of) →
get_term.py --obo_id <ID> --relations hierarchicalParents - Structures part of / hierarchical children →
get_term.py --obo_id <ID> --relations hierarchicalChildren - Compare direct vs hierarchical parents →
get_term.py --obo_id <ID> --relations parents,hierarchicalParents - Search for a term (e.g., "apoptosis" in GO) →
search_ols.py --query "..." --ontology <id> - Find a GO term matching a function →
search_ols.py --query "..." --ontology go --exact - Search in MONDO, CHEBI, CL, UBERON →
search_ols.py --query "..." --ontology <id> --defining - Paginate search results / next page →
search_ols.py --query "..." --rows N --start <offset> - Autocomplete a partial name →
suggest_ols.py --query "..." - Ontology metadata (e.g., EFO info) →
get_ontology.py --id <id> - OLS index statistics →
get_stats.py
Multi-step queries (e.g., "What is the parent of myocardial infarction?"): When the user names a term but you don't know its OBO ID, complete in exactly 2 steps — do NOT search across multiple ontologies:
- Search in the single most appropriate ontology:
search_ols.py --query "myocardial infarction" --ontology doid --exact --rows 1 --output /tmp/step1.json- Get relations using the OBO ID from step 1:
get_term.py --obo_id DOID:5844 --relations parents --output /tmp/step2.jsonOntology selection rule: ALWAYS use
doidfor common human diseases (e.g., diabetes, cancer),hpfor phenotypes,gofor gene functions,chebifor chemicals,uberonfor anatomy,clfor cell types. UsemondoONLY when cross-species context is explicitly mentioned or needed.
Utility Scripts
1. Search Terms Across Ontologies
Search for ontology terms by keyword and return clean JSON.
uv run scripts/search_ols.py --query "diabetes" \
--rows 5 --output /tmp/ols_search_results.json 2>/dev/null
Important:
--outputis required for all scripts. Results are always written to the specified file. For larger output, you can limit--rows(e.g., 5-10) or paginate using--start.
Returned Fields: JSON results include iri, label, description,
ontology_name, ontology_prefix, obo_id, short_form, type,
is_defining_ontology, and exact_synonyms.
Pagination: Output includes a pagination block with start, rows, and
has_more so you can decide whether to fetch more results.
Options:
--query: Search string (required). Searches labels, synonyms, descriptions, and identifiers.--ontology: Filter by ontology ID (e.g.,go,doid,efo,hp). Recommended when you know which ontology to search — avoids noise from 250+ ontologies.--type: Filter by entity type:class,property,individual, orontology.--exact: Flag for exact label match only. Use this for entity resolution when mapping a user's string to a specific ontology term ID.--defining: Only return terms from their defining (authoritative) ontology. E.g.,GO:0005634only from GO, not cross-referenced copies.--obsolete: Flag to include obsolete terms in results.--local: Only return terms in their defining ontology.--childrenOf: Restrict to children of given term IRI(s), comma-separated.--allChildrenOf: Restrict to all children including transitive relations (part of, develops from), comma-separated IRIs.--queryFields: Comma-separated fields to search in (e.g.,label,synonym,description).--fieldList: Comma-separated fields to return.--groupField: Group results by unique IRI.--isLeaf: Only return leaf terms (no children).--rows: Number of results to return (default 10).--start: Pagination offset (default 0).--output: File path to save results (required).
2. Autocomplete / Suggest
Get autocomplete suggestions for partial term names.
uv run scripts/suggest_ols.py --query "diabet" --rows 5 \
--output /tmp/ols_suggest.json 2>/dev/null
Options:
--query: Partial term to autocomplete (required).--ontology: Filter by ontology ID(s), comma-separated.--rows: Number of suggestions (default 10).--start: Pagination offset (default 0).--output: File path to save results (default: stdout).
3. Get Term Details
Retrieve full details for a specific ontology term by its OBO ID or IRI.
uv run scripts/get_term.py --obo_id "GO:0005634" \
--output /tmp/ols_term.json 2>/dev/null
Returned Fields: JSON includes iri, label, description, obo_id,
synonyms, ontology_name, is_obsolete, is_defining_ontology,
has_children, is_root, annotation, in_subset, and any requested
relations.
Summary Mode: Use --summary to get a clean, human-readable block on stdout
(Label, OBO ID, Ontology, Definition, Synonyms). The full JSON is always saved
to the --output file.
uv run scripts/get_term.py --obo_id "GO:0005634" --summary \
--output /tmp/nucleus_full.json
Options:
-
--obo_id: OBO-style identifier (e.g.,GO:0005634,DOID:9351). Mutually exclusive with--iri. Auto-converts to IRI with double encoding. -
--iri: Full IRI of the term. Mutually exclusive with--obo_id. -
--ontology: Ontology ID (auto-derived from--obo_idif not provided). -
--relations: Comma-separated list of relations to fetch.- Direct (is-a only):
parents,children,ancestors,descendants - Hierarchical (is-a + transitive like "part of", "develops from"):
hierarchicalParents,hierarchicalChildren,hierarchicalAncestors,hierarchicalDescendants - Graph:
graph— full graph JSON for a term
Note: Use hierarchical variants for anatomical/developmental ontologies (UBERON, CL) where transitive relations like "part of" and "develops from" are critical for navigating the hierarchy.
- Direct (is-a only):
-
--roots: List root terms of the ontology (requires--ontology). -
--preferred_roots: List preferred root terms (requires--ontology). -
--summary: Human-readable summary on stdout, full JSON to--output. -
--output: File path to save results (default: stdout).
4. Get Property Details
Retrieve details for an ontology property (relation type) with hierarchy.
uv run scripts/get_property.py --obo_id "BFO:0000051" --ontology go \
--output /tmp/ols_property.json 2>/dev/null
Options:
--obo_id: OBO-style ID of the property. Mutually exclusive with--iri.--iri: Full IRI of the property. Mutually exclusive with--obo_id.--ontology: Ontology ID (required with--iri).--relations: Comma-separated:parents,children,ancestors,descendants.--roots: List root properties of the ontology (requires--ontology).--output: File path to save results (default: stdout).
5. Get Individual Details
Retrieve details for an ontology individual (instance).
uv run scripts/get_individual.py --obo_id "IAO:0000103" --ontology iao --types \
--output /tmp/ols_individual.json 2>/dev/null
Options:
--obo_id: OBO-style ID. Mutually exclusive with--iri.--iri: Full IRI. Mutually exclusive with--obo_id.--ontology: Ontology ID (required with--iri).--types: Fetch the direct types (classes) of this individual.--alltypes: Fetch all types including ancestor classes.--output: File path to save results (default: stdout).
6. Get Ontology Information
List available ontologies or retrieve details for a specific one.
uv run scripts/get_ontology.py --id go \
--output /tmp/ols_ontology.json 2>/dev/null
Options:
--id: Specific ontology ID (e.g.,go,efo,doid). If omitted, lists all ontologies.--page: Page number for pagination (default 0).--size: Number of ontologies per page (default 20).--output: File path to save results (default: stdout).
7. Get OLS Statistics
Retrieve index statistics (total ontologies, classes, properties, individuals).
uv run scripts/get_stats.py --output /tmp/ols_stats.json 2>/dev/null
Options:
--output: File path to save results (default: stdout).
Reference
- API Reference: See references/api_reference.md for common ontology IDs, OBO ID format, and key API endpoints.
Workflow
- Use
suggest_ols.pyfor autocomplete when you have a partial term name. - Search for terms using
search_ols.py. Use--definingto prioritize authoritative definitions. Use--exactfor entity resolution. - If full details are needed, use
get_term.pywith the OBO ID or IRI. Use--summaryfor a concise view. - To explore a term's hierarchy, use
get_term.py --relations parents,childrenfor is-a only, or--relations hierarchicalParents,hierarchicalChildrenfor "part of" etc. - To explore from the top down, use
get_term.py --ontology go --roots. - For properties or individuals, use
get_property.pyorget_individual.py. - To discover available ontologies, use
get_ontology.py. - To check OLS index status, use
get_stats.py.
Related skills
More from google-deepmind/science-skills and the wider catalog.

encode-ccres-database
Query ENCODE cis-Regulatory Elements and experimental data via SCREEN GraphQL and REST APIs.

ensembl-database
Query Ensembl for gene IDs, sequences, structures, and variant effects.

foldseek-structural-search
Search for structurally similar proteins using 3D coordinate files against PDB, AlphaFold, and other databases.

gnomad-database
Query the Genome Aggregation Database (gnomAD) for variant frequencies, gene constraints, and structural variants.

gtex-database
Access RNA expression baselines and eQTL data from GTEx across 54 normal human tissues.

human-protein-atlas-database
Query protein expression levels, subcellular localization, and tissue distribution from the Human Protein Atlas.