@pipeworx/pubtator

Connect: https://pipeworx.io/mcp — every tool in the catalog, including @pipeworx/pubtator’s. Install: one-click buttons

No MCP client? Skip the connection: POST https://gateway.pipeworx.io/v1/tools/search_packs {"query":"..."} to find a tool below, GET /v1/tools/<name> for its schema, POST the same URL with arguments for the data — see For AI agents.

Tools: 5

Biomedical literature search by entity, plus the gene–disease–chemical–variant relations PubTator 3 has extracted from ~36M PubMed abstracts and PMC open-access full text — every relation labelled machine-extracted and carrying the sentences it was read from. Sourced from the PubTator 3 API run by NCBI / NLM. Connects gene-level lookups (mygene-info, pubmed) to the literature that mentions them.

Tools

• pubtator_find_entity(query, concept?, limit?) — resolve a gene, disease, chemical, variant, species or cell-line name to its normalized PubTator id (@GENE_BRCA1 / NCBI Gene 672, @DISEASE_Breast_Neoplasms / MeSH D001943). Answers “what is the id for X so I can query by it”.
• pubtator_search(query, page?) — literature search by free text, by entity ids joined with AND/OR, or by a relation query (relations:treat|@CHEMICAL_Doxorubicin|@DISEASE_Breast_Neoplasms). Each hit carries the matched sentence with its entity mentions decoded (text, entity, normalized ids, matched_query), PMID, PMCID, DOI, journal and date. 10 per page; total_results and total_pages returned.
• pubtator_relations(entity, type?, target?, limit?, evidence_for?) — the relations PubTator 3 extracted for an entity (which drugs treat a disease, which genes a disease is associated with …), ranked by supporting_publications. The top evidence_for relations (default 3, max 5) carry up to 3 supporting passages each. Accepts a PubTator id or a plain name (resolved via autocomplete; the match is reported in resolved_from).
• pubtator_relation_evidence(type, entity1, entity2, page?) — every passage supporting one specific relation, 10 per page, with PMIDs. Use it to audit a relation pubtator_relations returned.
• pubtator_annotations(pmids, full_text?) — entity annotations for up to 20 PMIDs: every mention in title + abstract (or PMC full text when full_text: true), normalized to NCBI Gene / MeSH / dbSNP / Taxonomy with character offsets, plus the relations extracted within each article.

Auth

Keyless.

Data sources

• https://www.ncbi.nlm.nih.gov/research/pubtator3-api/entity/autocomplete/?query=BRCA1&concept=gene&limit=10 — entity name → @TYPE_Name id, with the backing database id.
• https://www.ncbi.nlm.nih.gov/research/pubtator3-api/search/?text=…&page=1 — article search; text_hl is the matched sentence in PubTator’s inline entity encoding, decoded by this pack.
• https://www.ncbi.nlm.nih.gov/research/pubtator3-api/relations?e1=@GENE_BRCA1&type=associate&e2=… — extracted relations with supporting-publication counts.
• https://www.ncbi.nlm.nih.gov/research/pubtator3-api/publications/export/biocjson?pmids=31022191&full=true — BioC JSON with every annotation and in-article relation.

Things the next person would otherwise rediscover:

• Entity ids are name-based and case-sensitive. The API wants @GENE_BRCA1, @DISEASE_Breast_Neoplasms, @CHEMICAL_Doxorubicin — not @GENE_672 or @DISEASE_MESH_D001943. A search for the database-id form returns HTTP 200 with count: 0 and no error, which is why every entity argument in this pack goes through autocomplete when it does not start with @.
• Everything is a text-mining prediction. PubTator 3’s relations come from a neural relation-extraction model and its annotations from entity recognizers (GNormPlus, TaggerOne, tmVar…). Nothing is human-curated. Each relation and the annotations envelope carry an extraction field saying so; supporting_publications is the model’s evidence count, not a curation status.
• Relation queries ignore entity order. relations:associate|A|B and relations:associate|B|A return the same count. Twelve relation types: associate, cause, compare, cotreat, drug_interact, inhibit, interact, negative_correlate, positive_correlate, prevent, stimulate, treat.
• text_hl encoding. @<m>GENE_BRCA1</m> @GENE_672 @@@BRCA1@@@-mutated means: id tokens (query matches wrapped in <m>), then the surface mention wrapped in @@@. An id token is @ not followed by @@ — the first cut of the decoder treated @@@BRCA@@@ as an id and merged two mentions into one.
• Autocomplete concepts. gene, disease, chemical and variant are dense; species and cellline return [] for common names (“mouse”, “HeLa”). Drop the concept filter rather than concluding the entity is absent.
• Full text is large. full=true on a PMC open-access article returns 100+ passages (119 for PMID 31022191) and the document id becomes the PMC number; the PMID is recovered from the first passage’s article-id_pmid. Passages are truncated to 1,500 characters with truncated: true.
• Page size is fixed at 10 by the upstream; there is no size parameter.

Tools

• pubtator_find_entity — Resolve a gene, disease, chemical/drug, variant, species or cell-line name to its normalized PubTator 3 entity id (e.g. “BRCA1” -> @GENE_BRCA1, NCBI Gene 672; “breast cancer” -> @DISEASE_Breast_Neopla
• pubtator_search — Search PubMed/PMC literature by biomedical entity and get back, for each paper, the sentence that matched with its entity mentions decoded (gene, disease, chemical, variant, species, cell line — each
• pubtator_relations — List the relations PubTator 3 has extracted for a gene, disease, chemical or variant — which drugs treat a disease, which genes a disease is associated with, which chemicals inhibit a gene — ranked by
• pubtator_relation_evidence — The supporting passages behind one specific PubTator 3 relation — every paper whose text the model read the relation from, with the matched sentence, both entities’ mentions, PMID, journal and date. G
• pubtator_annotations — Entity annotations for specific PubMed articles: every gene, disease, chemical, variant, species and cell-line mention in the title and abstract (or PMC full text), each normalized to NCBI Gene / MeSH