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711,165 tools. Updated 2026-10-28 02:01

"Affinity" matching MCP tools:

  • Fetch N random trivia questions matching filters. Quality-first: by default excludes questions flagged for review (use quality='all' to include for audit/research). USE WHEN: building a quiz, sampling content for warmup, generating practice sets. NOT WHEN: you need a specific question ID (use quizbase_question_by_id) or want to explore a topic deeply with facets (use quizbase_topic_by_slug). KEY FILTERS: - amount: 1-50, default 10. - lang: ISO 639-1. Default "en". Supported: en, pl. Strict — unknown language returns 400. - category (slug): e.g. geography, history, science-and-nature. Full list via quizbase_categories. - difficulty: trivial | easy | medium | hard | expert. LLM-calibrated. Records not yet LLM-rated hold the importer placeholder (mostly "medium" for factoid sources). - type: multiple | boolean (default both; no text_input in random). - regions (cultural affinity, AND): empty in data = no cultural advantage assumed. Lowercase ISO 3166-1 alpha-2 ('us', 'pl', 'gb') + cultural codes ('jewish', 'christian-catholic', 'islam'). Filter for content statistically more likely known by residents/members. Discover via quizbase_regions. - source (array): include only these source databases (one or more of 12: opentdb, opentriviaqa, kqa-pro, entityq, mintaka, mkqa, nq-open, creak, qasc, arc, webq, quizbase). - exclude_source (array): drop these sources, e.g. ["entityq"] for human-curated only. Applied after source. - license (SPDX): CC-BY-SA-4.0 | CC-BY-SA-3.0 | MIT | etc. Restrict to redistribution-friendly content. - topic (curated slug): higher precision than tags. Alias resolver matches subcategories+tags. List via quizbase_topics. - topics_any: OR over curated topics, max 10. - tags (AND), tags_any (OR), subcategory: raw taxonomy. Use topic if available. - quality: 'high' (default, recommended) = cleanest, most broadly-useful. 'standard' = broader pool incl. niche/too-specific (more volume). 'all' = audit/research, includes flagged — when 'all', each question gains a "quality" field ('high' or 'needs_review'). - exclude (UUIDs, max 250): de-dupe within a quiz session. OUTPUT: { questions: [...], meta: { count, language } }. Each question carries full per-record attribution (source, author, license, licenseVersion, licenseUrl, sourceId, url, modifications, lastModified) — identical shape to REST /api/v1/questions/random. ATTRIBUTION REQUIRED if you redistribute. CC-BY-SA modifications must be credited per § 3(a)(1)(B) using each question's own attribution object. COMMON MISTAKES: forcing lang='pl' for a global audience (use 'en' default); skipping quality (default already excludes flagged content — only pass quality='all' for audit); using tags when a curated topic exists (worse precision).
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  • Cursor-paginated browse over the catalog. Quality-first: by default excludes questions flagged for review (use quality='all' for full pool). USE WHEN: full catalog sync, delta sync (updated_since), exhaustive enumeration by filter. NOT WHEN: you only need N random samples (use quizbase_random) or a single record (use quizbase_question_by_id). PAGINATION: stable cursor over id UUIDv7 DESC. First call: omit cursor. Next: pass meta.nextCursor. Stop when nextCursor is null. KEY FILTERS (full parity with REST): - lang: ISO 639-1, default "en". Supported: en, pl. - category (slug), difficulty (trivial|easy|medium|hard|expert — LLM-calibrated), type (multiple|boolean), subcategory (raw slug). - tags (AND), tags_any (OR, max 10): raw tag slugs. - topic (curated, alias resolver), topics_any (OR over curated): higher precision than tags. - regions (cultural affinity, AND): empty = no cultural advantage assumed. Lowercase ISO 3166-1 alpha-2 ('us', 'pl', 'gb') + cultural codes ('jewish', 'christian-catholic', 'islam'). Filter for content statistically more likely known by residents/members. Discover via quizbase_regions. - source (array): include only these of 12 (opentdb, opentriviaqa, kqa-pro, entityq, mintaka, mkqa, nq-open, creak, qasc, arc, webq, quizbase). - exclude_source (array): drop these sources, e.g. ["entityq"]. Applied after source. - license (SPDX): e.g. CC-BY-SA-4.0, MIT. - quality: 'high' (default) = cleanest, most broadly-useful. 'standard' = broader pool incl. niche/too-specific. 'all' = full pool incl. flagged; when 'all', each question gains a "quality" field ('high' or 'needs_review'). - updated_since (ISO 8601): only questions updated after this — for delta sync caches. BATCH + TRANSLATION MAPPING: - ids (up to 250): fetch those exact records in one call (anti-repeat, deep-links, restoring a saved set). Terminal selector — browse filters and cursor are ignored. Missing ids → meta.missing. - content_language (en|pl): with ids, returns each question's sibling in that CONTENT language across the translation chain — the same questions in another language. Distinct from lang (labels only). PAGINATION + COUNTING: - cursor (string): from previous meta.nextCursor. Omit for page 1. - limit (1-100, default 20). - count: none (default, skip — page via nextCursor) | exact (precise COUNT(*), index-only ~25-90ms). OUTPUT: { questions: [...], meta: { count, countMode, language, nextCursor, total? } }. Each question carries full per-record attribution (source, author, license, licenseVersion, licenseUrl, sourceId, url, modifications, lastModified) — identical shape to REST /api/v1/questions. ATTRIBUTION REQUIRED if you redistribute. Credit each question using its own attribution object — see license + licenseUrl + modifications fields per record. COMMON MISTAKES: not passing the cursor on subsequent calls (you'll re-read page 1); polling without updated_since when doing delta sync.
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  • 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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  • Recommended tracks for one or more seed tracks — the drop-in for Spotify's removed GET /v1/recommendations. Blends up to 5 catalog seed tracks into a single point in audio-feature space and returns the nearest catalogue tracks, RE-RANKED by genre affinity (so a feature-close cross-genre track doesn't outrank same-genre picks). Returns `seeds` (each {id, found}), `count`, and `tracks` (each {track, score, genre_relation}; each track carries its `genre`). `genre_relation` is "same", "compatible" (different but mixable family), "cross" (unrelated), or "unknown" (either side has no mapped genre), measured against the PRIMARY seed — the first of your seed_tracks we could actually use, so reordering seed_tracks changes it and a skipped seed never becomes the reference. With a SINGLE seed the field is the ranking's own verdict, so it explains the order (same as suggest_next_track). With SEVERAL seeds the ranking considers ALL of them while the label stays relative to your primary seed, so a "cross" label on a multi-seed call does NOT mean the track was pushed down — it may share a family with another of your seeds. `score` is the raw audio-feature cosine similarity in [0,1]; genre affinity influences the ORDER, not the score, so the list is NOT strictly score-descending. Use cross_genre=strict to return same-genre-family tracks ONLY (off-genre dropped server-side), or allow to disable the genre ranking. seed_tracks are catalog itunes_track_ids from search_catalog or the itunes_track_id field of a get_audio_features result. NO id? Pass `track` (+ optional `artist`) instead and we resolve the name to the best catalog match and seed on it — the resolved track is echoed back as `seed_query`; seed_tracks wins if both are given. TUNING: `min`/`max` are HARD filters and `target` is a preference (nearer ranks higher, nothing removed), over acousticness, danceability, duration_ms, energy, loudness, tempo and valence. NOTE: `popularity` (2026-08-27), `liveness`, `speechiness` (2026-09-26) and `instrumentalness` (2026-09-27) are RETIRED as tunables and return a 400 — popularity recorded how a track entered our catalogue, not audience size; the other three are returned as null for catalogue tracks. Values are FreqBlog's own (not Spotify's): energy and valence (models of how energetic / positive the preview sounds) on our own scales, danceability/acousticness on AcousticBrainz's scale — derive thresholds from our values rather than reusing Spotify-tuned ones. e.g. min={"tempo": 100}, max={"tempo": 130}, target={"energy": 0.8} for energetic 100-130 BPM tracks. When you tune, the response adds a `filters` block saying what applied, how many tracks each bound removed (`dropped_by`) and whether the bounds ran out of catalogue before `limit` (`limit_reached`) — if the list comes back short, read that BEFORE assuming the catalogue is thin. Costs 2 quota units.
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  • After MUTUAL_AFFINITY: the human-consent step. action 'status' shows where things stand (always free). 'accept' records your human's explicit YES together with the one contact channel they chose to share — the only place contact details are allowed; it stays sealed until BOTH humans consent, then each side receives the other's. 'decline' ends it neutrally and deletes anything provided; the counterparty never learns who declined. Decisions are immutable; the window closes after 14 days. Ask your human first — mutual agent affinity is not human consent.
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  • Return bounded rows for a dataflow — the completion path for hosted clients. Use this when the client cannot execute a client-download plan locally (no shell/filesystem — e.g. a hosted store app or connector) but still needs actual values. ``fetch`` server-side downloads the exact GET/fan-out plan ``build_url`` would produce, concatenates it, runs one bounded literal-equality query, and releases the artifact — the raw dataset never enters model context. Clients that CAN execute locally should keep using ``ask``/``build_url`` and run the plan themselves; ``fetch`` is the affinity-free hosted shortcut, not the power path. POST-only selections have no hosted download path: ``fetch`` raises ``fetch_shape_unsupported`` and the caller must execute the ``build_url`` plan client-side. A rejected/over-length selection raises the same reason ``build_url`` would report; narrow the selection and retry. **``fetch`` does not page, deliberately.** It holds no dataset between calls: every call rebuilds the plan, re-downloads every part from the provider, and releases the artifact. An offset over that would be unsound as well as wasteful — there is no snapshot behind the cursor, so rows shifting upstream between calls would silently skip or duplicate observations, and N pages would mean N full downloads of the same dataflow from an agency that may rate-limit. When a result is ``truncated``, narrow it (``select``, ``where``, ``time_range``) or switch to ``stage_url`` + ``query_dataset``, which pages with ``offset`` over ONE immutable staged artifact and downloads once. Rows are ordered by series key, then period, before ``limit`` applies. A series with a period that cannot be placed unambiguously keeps the provider's order. ``no_records_for_selection`` is TERMINAL, not a fault: the request was well-formed and the source holds no observations for it. Widen the selection or state that no data exists — do not retry the same selection. Only ``upstream_origin_error`` (an origin fault) and ``upstream_rate_limited`` are worth retrying.
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  • Sign-to-sign compatibility without birth data. Based on element and modality affinity. Fast — no ephemeris calculation required. WORKFLOW: BEFORE: None — no birth data needed. AFTER: asterwise_get_western_compatibility — when full charts are available. INPUT CONTRACT: sign1, sign2 — English zodiac names (Aries … Pisces). DO NOT CONFUSE WITH: asterwise_get_western_compatibility — requires full birth data, more accurate. asterwise_get_western_synastry — aspect geometry between two full charts. Full output and error contract: https://docs.asterwise.com/mcp/tools/get-western-zodiac-compatibility/
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  • Discover region codes used by the catalog. **Cultural affinity** — a question is tagged with a region if residents of that country, or members of that cultural/religious group, are statistically more likely to know the answer (NOT geography of the subject). USE WHEN: planning a quiz targeting users from a specific country or cultural background, exploring "what regions are represented". OUTPUT: array of {code, kind, label, count} sorted by count DESC. INPUTS: lang (en|pl), q (substring on code/label), kind (country|cultural), cursor, limit (max 500). Pair with quizbase_random or quizbase_list using `regions:[...]` to fetch matching questions.
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  • Sign-to-sign compatibility without birth data. Based on element and modality affinity. Fast — no ephemeris calculation required. WORKFLOW: BEFORE: None — no birth data needed. AFTER: asterwise_get_western_compatibility — when full charts are available. INPUT CONTRACT: sign1, sign2 — English zodiac names (Aries … Pisces). DO NOT CONFUSE WITH: asterwise_get_western_compatibility — requires full birth data, more accurate. asterwise_get_western_synastry — aspect geometry between two full charts. Full output and error contract: https://docs.asterwise.com/mcp/tools/get-western-zodiac-compatibility/
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  • Overall compatibility score (0–100) between two natal charts. Scores element affinity, synastry aspects between personal planets (Sun, Moon, Venus, Mars), and Sun/Moon/rising sign comparisons. WORKFLOW: BEFORE: asterwise_get_western_natal per person optional. AFTER: asterwise_get_western_synastry — drill into raw aspects if score needs detail. INPUT CONTRACT: person1, person2 — WesternBirthData each. DO NOT CONFUSE WITH: asterwise_get_western_synastry — raw aspects, no score. asterwise_get_western_zodiac_compatibility — sign-only, no birth data. Full output and error contract: https://docs.asterwise.com/mcp/tools/get-western-compatibility/
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  • SignalPulse: Institutional-grade trading & prediction-market intelligence for agents. Calibrated multi-engine reads across crypto, FX, macro-events, prediction markets (Polymarket/Kalshi/Manifold/PredictIt) and sports — de-vigged sportsbook consensus plus proprietary xG/EPA/Statcast/weather analytics. Agent analysis tier; the curated, sized, tracked calls are the premium service. Coverage: Global Endpoints: • sample (FREE): FREE pick-of-the-day — a full-depth sample of SignalPulse's sports intelligence on one featured matchup (de-vigged sportsbook consensus + proprietary stats/weather analytics). No payment, no API key. • game ($1.00): Deep single-match analysis for AI sports & research agents — a full multi-engine read of any game: de-vigged sportsbook consensus (Bovada/FanDuel/Pinnacle), sharp-line moves, proprietary xG/EPA/Statcast/map-pool analytics, venue weather and altitude physics, injuries and props. Returns 3 ranked +EV plays with full reasoning, props included. • predmarket ($2.00): Calibrated superforecaster scan of prediction markets for AI research & trading agents — a base-rate-anchored read across Polymarket, Manifold and PredictIt: cross-venue divergence, order-book and whale flow, and for sports the de-vigged sportsbook consensus plus proprietary team-strength and weather engines. Returns mispriced markets with probabilities, edge and full commentary, props included. • racing ($0.50): Today's GB/IRE racing card scanned for AI betting & research agents — the single highest expected-value selection plus value picks across every race, horse or greyhound. Horses: RPR/Topspeed, draw bias, pace shape, going×form and trainer/jockey conditional course/going records. Greyhounds: early-speed, trap affinity, overall run-time, track trap-bias. Live exchange + forecast prices give genuine EV. • h2h ($0.50): Player head-to-head for AI betting & research agents — a targeted read of TWO named players against each other in any matchup sport (golf, tennis, MMA). Returns the betting matchup verdict (favoured side, EV, edge) AND the fantasy head-to-head (who scores more DFS/fantasy points), plus data-backed props. • compare ($1.00): Compare & rank 2+ named players against each other for AI betting, fantasy & research agents — golf 3-balls, DFS player pools, season-long start/sit, 'best of these'. Returns a ranked list (each with a projection) plus the single best betting play AND the best fantasy/DFS value. • fantasy ($1.00): Direct fantasy advice for AI agents — start/sit, DFS lineup (salary cap, points-per-dollar), waiver pickups and trade analysis. FULLY OPEN: returns committed recommendations (who to start, accept/decline the trade), not just analysis. Projects fantasy points from the underlying data (golf SG/course-fit; NFL/MLB/NBA usage/role/matchup); the betting market is optional, never the gate. • player ($0.50): Single-player stat-projected outlook for AI betting, fantasy & research agents — data-backed prop projections (the stat is projected from the underlying data even when no book posts a line), the fantasy projection (points, floor/ceiling, start-worthiness) and the form/role/matchup read. Golf strokes-gained/course-fit; NFL/MLB/NBA usage/role/matchup. • ask ($1.00): Ask any sports or prediction-market question in plain language — the front door to SignalPulse's deep engines. Returns a data-grounded answer with an explicit DATA-vs-OPINION split (every claim cites its stat; judgment is labeled), a betting or fantasy angle when relevant, and a pointer to the deepest named endpoint. Honest when a question is outside coverage — it routes, it doesn't bluff. • golf ($1.00): Whole-field golf scan for AI betting, fantasy & research agents — reads the ENTIRE tournament field (PGA ShotLink strokes-gained + ESPN) and surfaces the single highest-EV play across every bet type (outright / each-way / top-N / matchup / make-cut / first-round-leader), led by course-fit and the tee-time weather wave. For named golfers use /api/scan/compare or /api/scan/h2h. • crypto ($2.00): Institutional-grade crypto market scan for AI financial & trading agents — 40+ live intelligence layers (regime, breadth, on-chain cycle, derivatives positioning, funding extremes, liquidation context, ETF/stablecoin flows) synthesized into a decision-ready read: directional bias, confidence, full rationale, key factors, adversarial pre-mortem. • market ($2.00): Institutional cross-asset market scan for AI financial & trading agents — multi-layer read across FX majors, metals, and equity indices: regime, COT positioning, yield spreads, carry, real yields, VIX term structure, options gamma, and macro, synthesized into a decision-ready read: best instrument, directional bias, confidence, full rationale, key factors. • forex ($0.50): Institutional forex market scan for AI financial & trading agents — multi-layer read across the 28 majors and crosses: rate differentials, COT positioning, carry, policy divergence, yield spreads, and cross-asset macro regime, synthesized into a decision-ready read: best pair, directional bias, confidence, full rationale, key factors. • event ($1.00): Institutional economic-event scan for AI financial & trading agents — historical-reaction study for a scheduled macro release (NFP, CPI, FOMC and more): how the affected FX pairs have moved after similar surprises, the surprise read, macro context, and which pair has the cleanest reaction profile, with directional bias, confidence, and full rationale. • options ($0.50): Institutional equity-options volatility scan for AI financial & trading agents — VRP (variance risk premium), IV term structure, GEX regime, max-pain and unusual options activity across liquid optionable names, synthesized into a decision-ready read: best ticker, vol/directional bias, confidence, full rationale, key factors. • futures ($0.50): Institutional futures market scan for AI financial & trading agents — multi-layer read across the futures complex (equity index, rates, energy, metals, grains): COT positioning (disaggregated + financial), seasonality, term structure, and macro regime, synthesized into a decision-ready read: best contract, directional bias, confidence, full rationale, key factors. • crypto-lite ($0.05): Crypto market tick — majors + sentiment + regime verdict
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  • Rank a followed X list exactly the way the digest ranker would, WITHOUT sending anything, and show the work: each unit carries its rank_signals (lane_fit = matches what the user builds/studies, affinity = an author they trust, engagement, substance, noise demotions) plus author groups (`group_size` + `related_items` = several posts by one author, best one leading). Use this to answer 'what's worth reading in my list?', 'why was this picked?', or to check how a preference change would land. `meta.inert_dimensions` names signals that had no data for this pool.
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  • Run market-basket analysis on a proposed basket / bundle to score its cohesion. Given 2+ products, returns a cohesion score 0..1 representing how strongly they bind together (their affinity) in the merchant's order data. Use this to vet a proposed bundle BEFORE recommending it, so agents can avoid suggesting bundles that look plausible but have no statistical signal. Also useful for 'is this a good bundle?', 'analyze this basket', or 'do these products go together?' questions.
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  • Overall compatibility score (0–100) between two natal charts. Scores element affinity, synastry aspects between personal planets (Sun, Moon, Venus, Mars), and Sun/Moon/rising sign comparisons. WORKFLOW: BEFORE: asterwise_get_western_natal per person optional. AFTER: asterwise_get_western_synastry — drill into raw aspects if score needs detail. INPUT CONTRACT: person1, person2 — WesternBirthData each. DO NOT CONFUSE WITH: asterwise_get_western_synastry — raw aspects, no score. asterwise_get_western_zodiac_compatibility — sign-only, no birth data. Full output and error contract: https://docs.asterwise.com/mcp/tools/get-western-compatibility/
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  • The account's own audiences: remarketing and customer lists, custom segments, combined segments. Each user list comes with its type, membership status (OPEN or CLOSED), size for Search and Display, eligibility and membership duration; custom and combined segments with their id and description. Insights flag lists below the 1,000 users Search needs and closed lists. Use the ids with draft_audience_attachment; for Google's in-market and affinity segments use search_audience_segments. Google Ads API: https://developers.google.com/google-ads/api/docs/start
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  • Lists the account's audiences and where its campaigns use them. Returns the audience lists (open or closed, size range, whether each is eligible for Search and Display, match rate for customer lists), custom and combined audiences, and for each enabled or paused campaign its audience setting (OBSERVATION, TARGETING or NOT_SET) and the audiences attached at campaign or ad group level, with clicks, conversions and cost. An ad group audience also carries its ad group's own setting, which Google allows only when the campaign has none. In-market and affinity segments are listed only for a name search. It reads only. Args: customer_id: The Google Ads customer ID (numbers only, no hyphens). campaign_id: Only this campaign's audiences. Optional; the account's lists are returned either way. start_date: First day of the clicks and cost (YYYY-MM-DD). Optional; with end_date. Default: the last 30 days. end_date: Last day of the clicks and cost (YYYY-MM-DD). Optional. segments: IN_MARKET or AFFINITY, to search Google's segment catalogue by name (with name_contains). Optional. name_contains: Text the segment names contain, e.g. "travel" (2 to 60 letters, digits, spaces or & , -); at most 50 segments come back. Required with segments. Returns: Dictionary with user_lists, custom_audiences, combined_audiences, campaigns (audience_mode, level, audiences with metrics and, at ad group level, the ad group's audience_mode), segments when searched, date_range and notes. Tools revision: d2872486.
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  • Score the cross-sell strength (product affinity) between two specific products. Returns the confidence the merchant's real co-purchase data supports for the pair, or a clear 'no signal' result when there's no qualifying rule. Use this to validate a proposed pair before recommending it, or to answer 'is X a good cross-sell for Y?', 'how strong is the affinity between X and Y?', or 'how often are X and Y bought together?'.
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  • Given a compound SMILES, the protein targets BindingDB has measured it (or a structurally similar compound) against, with species and affinity. AUTHORITATIVE for compound-to-target and off-target lookups grounded in published measurements. Use a similarity cutoff below 1.0 to pull analogues as well as the exact structure.
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  • List the quantitative ligand interactions for a protein target in the Guide to PHARMACOLOGY (IUPHAR/BPS). Given a GtoPdb target id (from search_targets), returns the ligands acting on it with interaction type (Agonist/Antagonist/Inhibitor/etc.), action, and binding affinity (e.g. pKi/pIC50). Requires a GtoPdb API key — register free at guidetopharmacology.org/login.jsp and pass your own via _apiKey.
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