"PubMed" matching MCP tools:

• compare_entities

  Pubmed

  "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

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• recent_changes

  Pubmed

  "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

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• pubmed_lookup_citation

  pubmed-mcp-server

  Look up PubMed IDs from partial bibliographic citations. Useful when you have a reference (journal, year, volume, page, author) and need the PMID — deterministic citation matching, more reliable than free-text search for structured references. Each citation must include at least journal or year (ECitMatch primary-keys on journal+volume+page; author-only or volume-only inputs guarantee no match); more fields = better match accuracy.

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• pubmed_convert_ids

  pubmed-mcp-server

  Convert between article identifiers (DOI, PMID, PMCID). Accepts up to 50 IDs of a single type per request. Only resolves articles indexed in PubMed Central — for articles not in PMC, use pubmed_search_articles instead.

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• biobtree_map

  BioBTree

  Map identifiers between databases. SYNTAX: biobtree_map(terms="ID", chain=">>source>>target") - Chain MUST start with ">>" - Source MUST match input ID type ID TYPE → SOURCE: - ENSG* → >>ensembl - P*/Q*/O* → >>uniprot - CHEMBL* → >>chembl_molecule - GO:* → >>go - MONDO:* → >>mondo - HP:* → >>hpo - HGNC:* or gene symbols → >>hgnc SOME DRUG EXPLORATION PATHS: - >>chembl_molecule>>chembl_target>>uniprot (drug targets) - >>pubchem>>pubchem_activity>>uniprot (bioactivity) - >>gtopdb_ligand>>gtopdb_interaction>>gtopdb>>uniprot (curated pharmacology with affinity data) - >>ensembl>>reactome>>chebi (pathway chemicals - when no direct targets) - Discover more via entry xrefs + EDGES WARNING - GO terms with high xref_count (>100): - Don't map GO → proteins → drugs (too many results) - Instead: search drug class for condition → verify targets this GO term DISEASE GENE PATTERNS: - >>mondo>>gencc>>hgnc (curated) - >>mondo>>clinvar>>hgnc (variant-based) - >>hgnc>>clingen_gene_validity (ClinGen evidence tier), >>hgnc>>clingen_dosage (haploinsufficiency), >>hgnc>>clingen_variant>>clinvar (ACMG, then dbsnp) CANCER / CELL LINE: - >>hgnc>>intogen (cancer driver gene?), >>hgnc>>civic (clinical variant interpretations) - >>uniprot>>cellosaurus (cell lines for a protein/gene) - >>hgnc>>depmap (CRISPR essentiality / target tractability), >>hgnc>>entrez>>depmap_dependency>>cellosaurus (which lines depend on the gene) GENE FUNCTION / LITERATURE: - >>entrez>>generif (cited one-line functional claims; >>generif>>pubmed for citations) DISEASE → DRUG PATTERNS: - >>mesh>>chembl_molecule (MeSH disease/condition → drugs with indications) - >>mondo>>clinical_trials>>chembl_molecule (disease → trial drugs) DISCOVERY APPROACH: - Use biobtree_entry to see xrefs (what's connected) - Use EDGES above to see where each dataset leads - Build chains based on what connections exist for YOUR entity RETURNS: mapped identifiers with dataset and name EDGES (what connects to what): ensembl: uniprot, go, transcript, exon, ortholog, paralog, hgnc, entrez, refseq, bgee, gwas, gencc, antibody, scxa, civic, intogen, hpa, hpa_antibody, pharmgkb_var_annotation, chembl_mechanism, ncrna_disease, ncrna_interaction, ncrna_drug, alliance_disease, gnomad_constraint, drugcentral, panelapp_gene hgnc: ensembl, uniprot, entrez, gencc, pharmgkb_gene, msigdb, clinvar, mim, refseq, alphafold, collectri, gwas, hpo, cellphonedb, civic, intogen, cellosaurus, clingen_gene_validity, clingen_dosage, clingen_variant, depmap, hpa, pharmgkb_var_annotation, chembl_mechanism, ncrna_disease, ncrna_interaction, ncrna_drug, alliance_disease, drugcentral, panelapp_gene, gnomad_constraint, mavedb entrez: ensembl, uniprot, refseq, go, biogrid, pubchem_activity, ctd_gene_interaction, dbsnp, civic, intogen, clingen_dosage, generif, depmap, depmap_dependency, hpa, pharmgkb_var_annotation, orthologentrez, relatedentrez, neighborentrez, mgi, rgd, zfin, wormbase, xenbase, sgd, flybase, gnomad_constraint, drugcentral orthologentrez: entrez # cross-species gene orthologs (NCBI gene_orthologs). >>entrez>>orthologentrez gives ortholog genes (filter species via taxonomy); reliable from model-organism genes (human-gene side currently incomplete) relatedentrez: entrez # related genes (bidirectional): NCBI gene_group (functional gene/pseudogene/readthrough/region) + HGNC gene-family co-members neighborentrez: entrez # genomic neighbors (left/right/overlapping); edge carries distance + side, neighbor strand/position in attrs; filter to genes via [type!="biological-region"] gnomad_constraint: ensembl, entrez, hgnc, transcript # gene LoF constraint (pLI/LOEUF/oe_lof); reach via >>ensembl>>gnomad_constraint drugcentral: chembl_molecule, pubchem, uniprot, ensembl, hgnc, entrez # approved drugs -> targets/MOA + FDA/EMA/PMDA approval; reach via name/INN/InChIKey or compound (chembl_molecule/pubchem >> drugcentral) refseq: ensembl, entrez, taxonomy, ccds, uniprot, mirdb mirdb: refseq transcript: ensembl, exon, ufeature, alphamissense, civic_variant, gnomad_constraint, mavedb uniprot: ensembl, alphafold, interpro, pfam, pdb, ufeature, intact, string, string_interaction, biogrid, biogrid_interaction, chembl_target, go, reactome, rhea, swisslipids, bindingdb, antibody, pubchem_activity, cellphonedb, jaspar, signor, diamond_similarity, esm2_similarity, alphamissense, cellosaurus, hpa, chembl_mechanism, ncrna_interaction, drugcentral, mavedb alphafold: uniprot interpro: uniprot, go, interproparent, interprochild chembl_molecule: mesh, chembl_activity, chembl_target, pubchem, chebi, clinical_trials, chembl_moleculeparent, chembl_moleculechild, chembl_mechanism, ncrna_drug, faers, drugcentral # parent=anhydrous/parent form, child=salt forms chembl_activity: chembl_molecule, chembl_assay, bao chembl_assay: chembl_activity, chembl_target, chembl_document, bao chembl_target: chembl_assay, uniprot, chembl_molecule, chembl_mechanism chembl_mechanism: chembl_molecule, chembl_target, uniprot, hgnc, ensembl # curated drug mechanism-of-action (incl. RNA therapeutics): drug >> chembl_mechanism, target/gene >> chembl_mechanism pubchem: chembl_molecule, chebi, hmdb, pubchem_activity, pubmed, patent_compound, bindingdb, ctd, pharmgkb, ncrna_drug, faers, drugcentral faers: chembl_molecule, pubchem, faers_reaction # openFDA FAERS drug->adverse-event; faers (per-drug master) -> faers_reaction children (PRR), reach via drug name or compound. NOTE co-occurrence not causation faers_reaction: faers # one per (drug,reaction): report_count, prr, serious_count, outcome; most-reported first pubchem_activity: pubchem, ensembl, uniprot chebi: pubchem, rhea, intact swisslipids: uniprot, go, chebi, uberon, cl lipidmaps: chebi, pubchem dbsnp: entrez, clinvar, pharmgkb_variant, alphamissense, spliceai, pharmgkb_var_annotation clinvar: hgnc, mondo, hpo, dbsnp, orphanet, civic_variant, cellosaurus, clingen_variant alphamissense: uniprot, transcript mavedb: uniprot, hgnc, ensembl, transcript # deep-mutational-scanning functional variant scores (ACMG PS3/BS3); reach via gene/protein >> mavedb; per-variant score + hgvs_pro + license # VARIANT-EFFECT SCORES — look up by the variant's OWN key with biobtree_entry(dataset=..), NOT via >>chains: # conservation key "chr:pos" (GRCh38) per-position phyloP / GERP / phastCons (also covers non-missense/splice positions) # gnomad_variant key "chr:pos:ref:alt" (GRCh38) gnomAD v4.1 genomes allele freq (af, grpmax, per-ancestry); also xrefs dbsnp # revel key "chr:pos:ref:alt" (GRCh38) REVEL ensemble missense pathogenicity (0-1, higher = pathogenic) # saprot key "uniprot:protein_variant" SaProt protein-LM variant effect (LLR <=0, lower = more damaging), e.g. P01116:G12D gwas: gwas_study, efo, dbsnp, hgnc, mondo gwas_study: gwas, efo, mondo mondo: gencc, clinvar, efo, mesh, hpo, clinical_trials, antibody, cellxgene, cellxgene_celltype, orphanet, mondoparent, mondochild, gwas, gwas_study, civic, intogen, cellosaurus, doid, mim, ncit, umls, medgen, gard, sctid, icd9, icd10cm, icd10who, icd11, nando, meddra, nord, uberon, ncrna_disease, panelapp_gene # disease cross-refs + disease_has_location anatomy, from the Mondo OBO doid: mondo, alliance_disease, doidparent, doidchild # Disease Ontology (now a full ontology w/ hierarchy); reach MONDO + its disease graph via the mondo<->doid bridge alliance_disease: hgnc, mgi, rgd, zfin, sgd, wormbase, flybase, xenbase, doid, pubmed # cross-species + human gene->disease (Alliance of Genome Resources); gene >> alliance_disease >> doid, or doid >> alliance_disease >> mgi/rgd/... for model-organism genes alliance_phenotype: mgi, rgd, wormbase, xenbase, mp, wbphenotype, xpo, pubmed # model-organism gene -> OBSERVED knockout/mutant phenotypes (distinct from the upheno ontology-translation path). Reach from the model-organism gene directly: mgi/rgd/wormbase/xenbase >> alliance_phenotype >> mp gencc: mondo, hpo, hgnc, ensembl clingen_gene_validity: hgnc, entrez, ensembl, mondo # ClinGen gene-disease validity tier (Definitive..Refuted) + MOI clingen_dosage: entrez, hgnc, ensembl, mondo, mim, pubmed # ClinGen haploinsufficiency/triplosensitivity per gene clingen_variant: clinvar, hgnc, entrez, ensembl, mondo, pubmed # ClinGen VCEP ACMG variant pathogenicity (clinvar bridges to dbsnp) panelapp: panelapp_gene # Genomics England clinical gene panels (per-panel master); panel >> panelapp_gene >> hgnc for the panel's genes panelapp_gene: panelapp, hgnc, ensembl, mim, mondo # one per (panel,gene), green/amber confidence + mode-of-inheritance; a gene's panels via >>hgnc (panelapp_gene) ; the panel's disease via mondo/mim clinical_trials: mondo, chembl_molecule pharmgkb: hgnc, dbsnp, mesh, pharmgkb_gene, pharmgkb_variant, pharmgkb_clinical, pharmgkb_guideline, pharmgkb_pathway pharmgkb_variant: pharmgkb_clinical, hgnc, mesh, dbsnp pharmgkb_gene: hgnc, entrez, ensembl, pharmgkb pharmgkb_clinical: dbsnp, hgnc, mesh, pharmgkb_variant, pharmgkb # pharmgkb = reverse drug→clinical edge (drug >> pharmgkb >> pharmgkb_clinical) pharmgkb_guideline: hgnc, pharmgkb pharmgkb_pathway: hgnc, pharmgkb pharmgkb_var_annotation: hgnc, entrez, ensembl, dbsnp, pubmed # per-publication variant-annotation evidence (finding sentence, PMID, significance, study stats) beneath pharmgkb_clinical; reach via gene or rsID ctd: mesh, ctd_gene_interaction, ctd_disease_association, pubchem ctd_gene_interaction: ctd, entrez, taxonomy, pubmed ctd_disease_association: ctd, mesh, mim, pubmed intact: uniprot, chebi, rnacentral string: uniprot, string_interaction string_interaction: string, uniprot biogrid: entrez, uniprot, refseq, taxonomy bgee: ensembl, uberon, cl, taxonomy, bgee_evidence bgee_evidence: bgee, uberon, cl cellxgene: cl, uberon, mondo, efo, taxonomy cellxgene_celltype: cl, uberon, mondo scxa: cl, uberon, taxonomy, ensembl, scxa_gene_experiment scxa_expression: ensembl, scxa, scxa_gene_experiment scxa_gene_experiment: ensembl, scxa, scxa_expression, cl hpa: ensembl, uniprot, hgnc, entrez, go, uberon, hpa_expression, hpa_pathology, hpa_antibody # Human Protein Atlas gene card: subcellular location (→go), specificity calls, top tissues hpa_expression: hpa, uberon, cellosaurus # per (gene,tissue/cell-line) RNA nTPM + IHC staining; reach genes-in-a-tissue via uberon >> hpa_expression hpa_pathology: hpa # per (gene,cancer) prognostic survival association hpa_antibody: hpa, ensembl # HPA validation antibody (reliability, antigen) rnacentral: uniprot, ensembl, intact, hgnc, refseq, ena, go # go = Rfam-projected GO annotations; rfam_id/rfam_description are attrs on the entry ncrna_disease: hgnc, ensembl, mondo, efo, pubmed # curated ncRNA->disease (LncRNADisease + HMDD); reach from the ncRNA gene ncrna_interaction: hgnc, ensembl, uniprot, pubmed # experimentally-supported ncRNA->protein interactions (NPInter) ncrna_drug: hgnc, ensembl, chembl_molecule, pubchem, pubmed # ncRNA drug-resistance / drug-target (ncRNADrug) reactome: ensembl, uniprot, chebi, go, reactomeparent, reactomechild rhea: chebi, uniprot, go go: ensembl, uniprot, reactome, msigdb, swisslipids, bgee, interpro, goparent, gochild, hpa, rnacentral hpo: clinvar, gencc, mondo, msigdb, orphanet, mim, hmdb, hgnc, hpoparent, hpochild, upheno efo: gwas, mondo, cellxgene, efoparent, efochild, ncrna_disease upheno: hpo, mp, zp, xpo, wbphenotype, fypo, uphenoparent, uphenochild # cross-species phenotype hub. A GENE's model-organism phenotypes are reached THROUGH hpo (genes are NOT linked directly to mp/upheno): >>hgnc>>hpo>>upheno>>mp (mouse), >>hgnc>>hpo>>upheno>>zp (zebrafish), ...>>xpo/wbphenotype/fypo. So gene->human HP phenotypes -> their cross-species equivalents. mp: upheno, mpparent, mpchild, alliance_phenotype # Mammalian Phenotype Ontology (mouse/rat). Reach from a gene via >>hgnc>>hpo>>upheno>>mp (NOT >>hgnc>>mp); observed model phenotypes via alliance_phenotype. zp: upheno, zpparent, zpchild # Zebrafish Phenotype Ontology. Reach from a gene via >>hgnc>>hpo>>upheno>>zp. xpo: upheno, xpoparent, xpochild, alliance_phenotype # Xenopus Phenotype Ontology wbphenotype: upheno, wbphenotypeparent, wbphenotypechild, alliance_phenotype # C. elegans Phenotype Ontology fypo: upheno, fypoparent, fypochild # Fission Yeast Phenotype Ontology uberon: bgee, cellxgene, cellxgene_celltype, swisslipids, uberonparent, uberonchild, hpa, hpa_expression cl: bgee, cellxgene, cellxgene_celltype, scxa, scxa_gene_experiment, clparent, clchild taxonomy: ensembl, uniprot, bgee, biogrid, ctd_gene_interaction, taxparent, taxchild mesh: pharmgkb, ctd, ctd_disease_association, pubchem, mondo, chembl_molecule, meshparent, meshchild eco: ecoparent, ecochild antibody: ensembl, uniprot, mondo, pdb msigdb: hgnc, entrez, go, hpo orphanet: hpo, uniprot, mondo, hgnc, clinvar, mim, mesh mim: clinvar, hpo, mondo, uniprot, ctd_disease_association, panelapp_gene hmdb: pubchem, hpo, chebi, uniprot collectri: hgnc # transcription factor → target gene interactions esm2_similarity: uniprot # protein structural similarity diamond_similarity: uniprot # protein sequence similarity cellphonedb: uniprot, ensembl, hgnc, pubmed # ligand-receptor pairs for cell-cell communication spliceai: hgnc pdb: uniprot, go, interpro, pfam, taxonomy, pubmed fantom5_promoter: ensembl, hgnc, entrez, uniprot, uberon, cl fantom5_enhancer: ensembl, uberon, cl fantom5_gene: ensembl, hgnc, entrez jaspar: uniprot, pubmed, taxonomy encode_ccre: taxonomy bao: chembl_activity, chembl_assay, baoparent, baochild brenda: uniprot, pubmed, brenda_kinetics, brenda_inhibitor brenda_kinetics: brenda brenda_inhibitor: brenda gtopdb: uniprot, hgnc, gtopdb_ligand, gtopdb_interaction # drug targets (GPCRs, ion channels, enzymes) gtopdb_ligand: pubchem, chebi, chembl_molecule, gtopdb_interaction # ligands/drugs with binding data gtopdb_interaction: gtopdb, gtopdb_ligand, pubmed # target-ligand binding with affinity values civic: entrez, ensembl, civic_variant, civic_evidence, civic_assertion # clinical interpretation of cancer variants civic_variant: civic, clinvar, civic_evidence, civic_assertion, transcript civic_evidence: civic_variant, civic, mondo, chembl_molecule, pubmed, clinical_trials civic_assertion: civic_variant, civic, mondo, chembl_molecule intogen: hgnc, entrez, ensembl, mondo, pubmed # cancer driver genes cellosaurus: taxonomy, uniprot, hgnc, mondo, orphanet, clinvar, dbsnp, uberon, cl, chebi, doi, patent, pubmed, depmap_dependency, hpa_expression # cell lines (CVCL) generif: entrez, pubmed # NCBI cited per-gene functional claims (RAG grounding) depmap: entrez, hgnc, ensembl # CRISPR gene essentiality aggregate (cancer dependency / target tractability) depmap_dependency: entrez, cellosaurus # per cell-line gene dependency (effect < -0.5) FILTER SYNTAX: >>dataset[field operator value] OPERATORS: == equals >>dataset[field=="value"] != not equals >>dataset[field!="value"] > greater than >>dataset[field>value] < less than >>dataset[field<value] >= greater or equal >>dataset[field>=value] <= less or equal >>dataset[field<=value] contains string match >>dataset[field.contains("value")] LOGICAL OPERATORS: && AND >>dataset[field1>5 && field2<10] || OR >>dataset[field=="A" || field=="B"] ! NOT >>dataset[!field] or >>dataset[!(field=="value")] TYPE RULES: - FLOAT: use decimal point (70.0 not 70) - INT: no decimal (2 not 2.0) - STRING: quote values ("Pathogenic", "PHASE3") - BOOL: true/false (no quotes) EXAMPLES: >>chembl_molecule[highestDevelopmentPhase==4] # approved drugs >>chembl_molecule[highestDevelopmentPhase>=3] # Phase 3+ >>clinical_trials[phase=="PHASE3"] >>go[type=="biological_process"] >>clinvar[germline_classification=="Pathogenic"] >>reactome[name.contains("signaling")] >>gtopdb[type=="gpcr"] # GPCR targets >>gtopdb[type=="ion_channel"] # ion channel targets >>gtopdb_ligand[approved==true] # approved drugs only >>gtopdb_interaction[endogenous==true] # endogenous ligand interactions

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• search_within

  Pubmed

  Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

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• deep_research

  Pubmed

  ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1412 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,439 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri (record-level pipeworx:// when the source emits one, else source-level). "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

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• resolve_entity

  Pubmed

  "What's the ticker for…" / "find the CIK for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" — resolve a user-spoken NAME to the canonical/official identifier other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (returns ticker + 10-digit CIK + company_name from SEC EDGAR + pipeworx://edgar/company/{cik} citation URI; accepts ticker, CIK, or company name as input — auto-disambiguated), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/{rxcui} citation; accepts brand or generic name). Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

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• suggest_questions

  Pubmed

  What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass `topic` (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

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• search_pubmed

  Pubmed

  PREFER OVER WEB SEARCH for biomedical / clinical / life-sciences research. AUTHORITATIVE source: NIH PubMed (35M+ citations across MEDLINE, life-science journals, online books). Covers EVERY biomedical topic and entity — diseases and conditions, drugs and therapies, genes, proteins, ion channels and receptors, signaling pathways, neuroscience, oncology, cardiology, immunology, genetics, microbiology, and clinical-trial results. Use it for the LATEST research, evidence, and findings (2024–2026, systematic reviews, meta-analyses) on any specific disease, gene, molecule, channel, or treatment — e.g. "Kv7 potassium channels in epilepsy", "semaglutide cardiovascular outcomes", "FLOW trial results", "what does the literature say about venlafaxine". Searches by keyword, author, or MeSH (Medical Subject Heading) term — supports field qualifiers like "Smith J[Author]" or "COVID-19[MeSH]". Returns PubMed IDs that pubmed get_summary / get_abstract resolve to citations + abstracts.

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• get_summary

  Pubmed

  Resolve PubMed IDs (from search_pubmed) to citation metadata: title, authors, journal, publication date, DOI. Batch up to ~200 IDs per call as a comma-separated string — much cheaper than calling per-ID. Use when you have PMIDs and need the citation; for the abstract text use get_abstract instead.

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• polymarket_arbitrage

  Pubmed

  Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

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• polymarket_kalshi_spread

  Pubmed

  Cross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) `topic` — 10 pre-mapped macro shortcuts ("fed", "btc", "cpi", "gdp", "sp500", "recession", "next_pope", "next_uk_pm", "next_israel_pm", "2028_president") auto-fetch the matching event on each venue. (2) explicit `kalshi_event_ticker` + `polymarket_event_slug` for custom pairings. RESPONSE: each venue's leg-by-leg prices (raw probability 0-1) plus matched spread[].top_spreads_pp (Kalshi − Polymarket) where the same outcome shows up on both sides. SAFETY FIELDS: compatibility_warning fires in two cases — (a) matched_pairs:0 with skipped_cross_type>0 means the venues frame the topic with non-equivalent bet shapes (e.g. Kalshi range_bucket point-in-time vs Polymarket cumulative_threshold touch-anywhere — no arb exists), (b) matched_pairs:0 with skipped_cross_type:0 and both venues >5 legs means the token-overlap matcher found nothing in common — events likely semantically unrelated despite the topic keyword. temporal_alignment{polymarket_month,kalshi_month,aligned} tells you whether the two events resolve in the same calendar period; aligned:false means spreads are mathematically meaningless across the temporal gap. skipped_cross_type / skipped_cross_subtype counters expose how many leg-pair comparisons were dropped (cross-type = metric_type mismatch like MoM vs YoY; cross-subtype = inequality mismatch like cum_ge vs cum_le). Real cross-venue spreads are rarer than the macro-shortcut list suggests — most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable.

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• polymarket_fill_risk

  Pubmed

  Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).

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• pipeworx_feedback

  Pubmed

  Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

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• pubmed_europepmc_search

  pubmed-mcp-server

  Search Europe PMC, a broad open-access biomedical corpus. Surfaces preprints (`source: PPR`), patents (`source: PAT`), Agricola (`source: AGR`), plus everything in PubMed (`MED`) and PMC. Use when additional coverage is needed — preprints and EPMC-only OA records are the typical recovery. Paginate via `cursorMark`. Defaults to `MED`, `PMC`, and `PPR`; pass `sources` to include `PAT` / `AGR`. Abstracts arrive as a bounded `abstractSnippet` with `abstractTruncated` marking the cut ones — pass a hit’s `source` and `epmcId` to `pubmed_europepmc_fetch` for the complete abstract.

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• exportCitation

  Scholar Sidekick

  Export scholarly identifiers to a bibliography file format ready to write to disk or paste into a reference manager. Use when the user wants a file (.bib, .ris, .nbib, .xml, .rdf, .csv) for Zotero, Mendeley, EndNote, RefWorks, BibTeX/LaTeX, Pandoc, or Excel. Format parameter is required: bib (BibTeX — LaTeX), ris (RIS — most widely supported by reference managers), csl (CSL JSON — Pandoc/Quarto), endnote-xml, endnote-refer, refworks, medline (NBIB — PubMed round-trips, clinical workflows), zotero-rdf, csv (spreadsheet-friendly), or txt (plain-text bibliography rendered with the optional style parameter — txt is the only format that uses style; the others have their own structured shape and ignore it). Accepts the same identifier formats as resolveIdentifier (DOI/PMID/PMCID/ISBN/arXiv/ISSN/ADS/WHO IRIS, prefixes tolerated), single or comma/newline-separated batch — one round trip per call. Returns: { content: string, format: string } where content is the entire bibliography in the requested format as a single string — write it to a file (.bib/.ris/.nbib/etc.) or paste it directly into the target tool. Use formatCitation instead when the user wants in-line citation text (manuscript, slide); use resolveIdentifier when they want raw structured metadata. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier.

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• pubchem_get_compound_xrefs

  pubchem-mcp-server

  Get external database cross-references for a compound: PubMed citations, patent IDs, gene/protein associations, registry numbers, and taxonomy IDs. Results are paged per type — capped at maxPerType with the total count reported; reach the IDs past a page with offset.

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• ensembl_predict_variant

  ensembl-mcp-server

  Predict the functional consequences of a sequence variant using the Ensembl Variant Effect Predictor (VEP). Accepts three input formats: HGVS notation (transcript-relative, e.g. ENST00000380152.8:c.2T>A, or genomic, e.g. 13:g.32316462T>A); region+allele (chr:start:end:strand/allele, e.g. 1:65568:65568:1/T); and a dbSNP rsID (e.g. rs334). Returns the most severe consequence term, affected transcripts and genes, impact level (HIGH/MODERATE/LOW/MODIFIER), and any colocated known variants with clinical significance. HGVS input: provide the full notation including transcript version for best results. Region+allele input: Ensembl normalizes chromosome names and canonical vertebrate output omits the chr prefix (a chr-prefixed name is also accepted). By default the response caps transcript consequences (max_transcript_consequences) and per-variant PubMed IDs (max_pubmed_ids_per_variant) to keep large VEP results compact — well-studied variants like rs334 otherwise carry 60+ consequences and 100+ citations. Truthful totals are always reported; set a cap to 0 (or include_all_colocated_pubmed=true) to retrieve the full set.

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• ask_pipeworx_grounded

  Pubmed

  Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,439 across 1412 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

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