Evidence-based supplement research for AI assistants. Query 4,794 clinical effect edges across 290 supplements and 502 biomarkers, 765 supplement-supplement interactions, 1,634 FDA drug interactions, and 22,570 product quality scores with AviScore ratings. 8 tools: supplement and biomarker lookup, drug and supplement interactions, brand quality rankings, condition-based supplement recommendations, fuzzy search for supplements and health conditions.
Scored across 11 tools
Most tools occupy clearly distinct slots: name resolution (search_supplements, search_conditions), directional evidence lookups (supplement→biomarker, biomarker→supplement, condition→supplement), interaction checking, brand ranking, and stack building. Two pairs are borderline overlapping: get_supplement_interactions vs check_interaction, and get_supplements_for_biomarker vs get_interventions_for_biomarker, but each description explicitly cross-references the other and states the scope difference (full list vs point check; supplement-only vs all intervention types).
All names are snake_case verb_noun with a small, stable verb set (get_/search_/check_/build_) and a predictable X_for_Y resource pattern for the directional lookups. No mixed conventions or vague verbs; the name alone tells you the resource and direction.
11 tools for a supplement-evidence domain with multiple resource types (supplements, biomarkers, conditions, interactions, products) and two lookup directions is well-scoped. Each tool has a distinct job: resolution, retrieval, interaction checking, ranking, and composition.
The surface covers the core workflows: resolve entity → get evidence (both directions) → check interactions → find products → assemble a stack, including safety contraindications and coverage caveats. Minor gaps exist (no condition-description/info tool, no reverse brand→supplement lookup, no explicit goal listing), but agents can work around these via the existing search and evidence tools.
Starter stack (default 3, max 5) of recommendable supplements for a goal (e.g. 'insomnia', 'LDL cholesterol'), best-replicated app-visible evidence first; never Rx or research; biomarker-proxy rows are hypotheses. Not offered for cancer or other serious disease (no_match). One item per ingredient family, checked for supplement interactions with each other and with exclude, which also takes medications by generic name (checked against FDA labels: major picks dropped, moderate flagged; unlisted medications are NOT checked, so ask, then use get_supplement_interactions with drug). Each item: why, grade, study dose (context), PMIDs, cited cautions (an avoid-level caution leads as cautions_summary and in data.important_cautions: state those first), same-mechanism and interaction flags. include_claim_link is opt-in (default false): true stores a small claim record and returns data.claim.claim_url for saving the stack in a free Aviado account.
Inputs: goal (required), exclude, max_items, include_claim_link.
Point check of one pair, about 1-3 KB: a and b are each a supplement (name or canonical_id) or a medication (generic name; salt forms merged, brand names not matched). Supplement + supplement uses Aviado's verified pairs only (components and classes included); supplement + medication uses FDA drug-label rows only; two medications are not covered (no_match). Returns verdict {found, severity, direction, summary, recommendation, recorded_for, evidence_level, sources} and coverage (level none/sparse/moderate/broad). found:false is NOT a safety answer: an empty result is not evidence that there is no interaction, so read coverage.note and say so. Check status first: needs_confirmation / no_match means the input did not resolve, data is null and next_step says how to retry.
Inputs: a (required), b (required).
Biomarkers a supplement moves in the Aviado app's evidence graph, one entry per biomarker (strongest edge), ordered by prior_weight (graph evidence weight 0-1, not a calibrated probability or effect size). Each entry has direction (up/down/bidirectional), goal/subgoal, has_verified_support / mixed_evidence, up to 3 PubMed IDs (pmids; URL template in coverage) with n_studies / rct_count, and dose (mean study dose {value, unit}: evidence context, not a dosing recommendation; see coverage.dose_note); null fields are omitted. Partial and non-exhaustive: a missing biomarker is not evidence of no effect. The supplement may be a name or an Aviado canonical_id (from search_supplements or a candidates list). Check status first: needs_confirmation / no_match means the input did not resolve, data is null and next_step says how to retry.
Inputs: supplement (required), limit.
All mixed interventions in Aviado's evidence graph that move a biomarker, each labelled with intervention_type: supplement, food_substance, prescription_drug, research_compound, research_nootropic, medical_compound, form_dependent, not_recommendable or unknown (omitted on plain recommendable supplement rows; see coverage.row_defaults). Rows that are not supplements are context, never a recommendation, and carry no dose or pathway details. Rows carry up to 3 PubMed IDs (pmids; URL template in coverage); optional direction (up/down) filters. graph_status (proven/suspected) is an internal eligibility label, not clinical validation; pathway_tags are raw graph metadata, not validated mechanisms. Coverage is partial and non-exhaustive; absence is not evidence of no effect (see coverage). The biomarker may be a name, an abbreviation, an Aviado id or a LOINC code (e.g. '4548-4'). Check status first: needs_confirmation / no_match means the input did not resolve, data is null and next_step says how to retry.
Inputs: biomarker (required), limit, direction.
Overview of one supplement: description, typical uses, its top 5 biomarkers (with up to 3 PubMed citations each), goals, mechanism tags and cited safety contraindications. Contraindications include those recorded for what it contains (e.g. turmeric shows curcumin's), labelled with recorded_for; only cited rows are shown, so an empty list is not proof of safety. For all biomarkers use get_biomarkers_for_supplement; for drug interactions use get_supplement_interactions. The supplement may be a name or an Aviado canonical_id (from search_supplements or a candidates list). Check status first: needs_confirmation / no_match means the input did not resolve, data is null and next_step says how to retry.
Inputs: supplement (required).
Drug interactions come only from FDA drug labels and supplement-supplement ones only from Aviado's verified pairs; an empty result is not evidence that there is no interaction. Labels are thorough for some supplements and sparse or missing for most; read coverage.drug, and never treat an empty or short list as proof of safety. To check one medication pass drug (generic name; salt forms merged, so 'warfarin' matches 'warfarin sodium'; brand names are not matched). Also returns supplement-supplement interactions from Aviado's verified pairs only (synergies, antagonisms, absorption conflicts, timing), except on a drug check unless include_supplement_interactions is true. Interactions recorded for what the supplement contains are included and labelled: fish oil returns EPA, DHA and omega-3 interactions (interaction_sources, recorded_for). Drug entries are one per drug, most severe first, paged with drug_limit/drug_offset (drug_page.total, next_offset); label_artifact_suspected marks an entry whose drug name contains the supplement only as a salt (e.g. esomeprazole magnesium) and whose text does not mention it. Data-source failures return an error, never an empty list. The supplement may be a name or an Aviado canonical_id (from search_supplements or a candidates list). Check status first: needs_confirmation / no_match means the input did not resolve, data is null and next_step says how to retry.
Inputs: supplement (required), drug, drug_limit, drug_offset, +2 more.
Supplements in Aviado's evidence graph that move a biomarker (e.g. 'LDL cholesterol', 'HbA1c'). Lists only registry-classified supplements: entities Aviado's identity vocabulary types as supplement, class or combination and marks recommendable; prescription drugs, research compounds, foods and unknown ids are excluded (use get_interventions_for_biomarker for all of them). Each hit has direction (up/down/bidirectional), prior_weight (evidence confidence 0-1, not effect size), graph_status (proven/suspected: internal eligibility labels, not clinical verification), has_verified_support / mixed_evidence, and dose (mean study dose {value, unit}: evidence context, not a dosing recommendation; see coverage.dose_note). Rows carry up to 3 PubMed IDs (pmids; URL template in coverage) with n_studies / rct_count; optional direction (up/down) filters. Null fields and default typing (coverage.row_defaults) are omitted per row. Results are partial and non-exhaustive; pathway_tags are raw graph metadata, not validated mechanisms. The biomarker may be a name, an abbreviation, an Aviado id or a LOINC code (e.g. '4548-4'). Check status first: needs_confirmation / no_match means the input did not resolve, data is null and next_step says how to retry.
Inputs: biomarker (required), limit, direction.
Supplements associated with a health condition or goal (e.g. 'insomnia', 'anxiety', 'joint pain'), strongest evidence first. evidence_basis direct rows come from studies of the condition; biomarker_proxy rows are inferred from the supplement's effect on the named proxy_biomarkers (each backed by an effect the Aviado app shows), are hypotheses, and are graded at most D. Only recommendable supplements are listed by default (rows carry intervention_type, entity_type, regulatory_status and recommendable only where they differ from coverage.row_defaults; null fields are omitted); include_non_supplements lists prescription drugs and research compounds too, labelled, as context only. dose is a typical study dose {value, unit}: evidence context, not a dosing recommendation (coverage.dose_note). Partial: absence is not evidence a supplement does not help. A weak or ambiguous condition name returns needs_confirmation with candidate conditions; confirm one and pass its id. Check status first: needs_confirmation / no_match means the input did not resolve, data is null and next_step says how to retry.
Inputs: condition (required), limit, min_grade, include_non_supplements.
Highest-quality brands for a supplement: one product per brand (its best), ranked by ProofMark, Aviado's product quality score (testing, ingredients, transparency, manufacturing, certifications). proofmark_100 is the 0-100 score (proofmark = proofmark_100/100) with a letter grade; proofmark_status is graded, below_coverage_floor (assessed but too little evidence to score; not a low score) or not_scored, and only graded products carry a score. Retired catalog products are excluded. match_basis says whether products matched the supplement identity or a text search (less precise; may include multi-ingredient products). price, retailer and product_url come from scraped listings and may be missing or stale. The supplement may be a name or an Aviado canonical_id (from search_supplements or a candidates list). Check status first: needs_confirmation / no_match means the input did not resolve, data is null and next_step says how to retry.
Inputs: supplement (required), limit.
Find the condition id for a health condition, symptom or goal. Answers when the name matches one condition clearly (data.conditions lists it first, with up to 4 related suggestions); a weak or ambiguous name (e.g. 'blood pressure': high vs low) returns needs_confirmation with the suggestions in candidates. Ask the user which one they mean and pass its id as condition to get_supplements_for_condition.
Inputs: query (required).
Find the Aviado id for a supplement name, exactly (no fuzzy matching). Answers with matches[0] = the identity: vocabulary_exact, or user_intent for salt/form names ('magnesium citrate' -> magnesium, with a form note). A name Aviado does not hold may be answered with a naming-model suggestion (match_type naming_slm_suggestion, identity false); confirm it with the user before using its id. Family names ('lithium') and confirm-only names return needs_confirmation with the members or candidates in candidates. Each match carries entity_type, regulatory_status and recommendable. Use mode='supplement_retail' only for raw retail product titles. Pass the confirmed canonical_id to the other supplement tools.
Inputs: query (required), mode.
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