Post-market safety surveillance and recall/adverse-event RETRIEVAL across the full spectrum of FDA-regulated products that are NOT covered by the drug-AE signal skills: medical devices, food / dietary supplements / cosmetics, veterinary drugs, and drug supply (shortages). Orchestrates openFDA endpoints (MAUDE device adverse events + device recalls + 510(k), CAERS food/supplement/ cosmetic adverse events, veterinary adverse events, drug shortages, and cross-product enforcement/recall reports). USE WHEN the user asks: "are there adverse events for [device / pacemaker / infusion pump / insulin pump]", "device recalls for [firm/product]", "supplement / vitamin / cosmetic adverse reactions", "is [drug] in shortage", "what injectables are on shortage", "veterinary / animal adverse events for [drug] in [dog/cat/horse]", "food recall for listeria", "MAUDE report for [device]", "CAERS reactions for [brand]". DO NOT USE for drug adverse-event SIGNAL detection or disproportionality (PRR / ROR / IC) or drug-AE associatio
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name
tooluniverse-product-safety-surveillance
description
Post-market safety surveillance and recall/adverse-event RETRIEVAL across the full spectrum of FDA-regulated products that are NOT covered by the drug-AE signal skills: medical devices, food / dietary supplements / cosmetics, veterinary drugs, and drug supply (shortages). Orchestrates openFDA endpoints (MAUDE device adverse events + device recalls + 510(k), CAERS food/supplement/ cosmetic adverse events, veterinary adverse events, drug shortages, and cross-product enforcement/recall reports). USE WHEN the user asks: "are there adverse events for [device / pacemaker / infusion pump / insulin pump]", "device recalls for [firm/product]", "supplement / vitamin / cosmetic adverse reactions", "is [drug] in shortage", "what injectables are on shortage", "veterinary / animal adverse events for [drug] in [dog/cat/horse]", "food recall for listeria", "MAUDE report for [device]", "CAERS reactions for [brand]". DO NOT USE for drug adverse-event SIGNAL detection or disproportionality (PRR / ROR / IC) or drug-AE association scoring — that is `tooluniverse-pharmacovigilance` / `tooluniverse-adverse-event-detection`. This skill is multi-product surveillance and retrieval, not drug-AE statistical signal mining.
Retrieve and interpret post-market safety records across every FDA-regulated
product class except drug-AE signal mining: medical devices, food / dietary
supplements / cosmetics, veterinary drugs, and drug supply (shortages), plus
cross-product enforcement/recall reports.
KEY PRINCIPLES
Decide the product class first. Device? Food/supplement/cosmetic? Vet drug? Drug shortage? Recall? The class picks the tool.
Build a valid Lucene query. openFDA uses field-scoped terms; combine with a space-separated . Phrases and special characters need care (see Query Grammar).
field:value
AND
Retrieve, then interpret. These are spontaneous/voluntary reports. Report the records and their fields; never assert causation or rates.
Cite every record with the tool name, the openFDA endpoint, the query used, and the total hit count from meta.results.total.
Stay in scope. If the request is drug-AE signal detection (PRR/ROR/IC), STOP and point to tooluniverse-pharmacovigilance / tooluniverse-adverse-event-detection.
Nested fields use dot paths:device.generic_name:pacemaker, products.industry_name:Cosmetics, animal.species:Dog, reaction.veddra_term_name:Vomiting, drug.active_ingredients.name:carprofen.
Combine terms with a SPACE-separated AND (verified working): device.generic_name:pacemaker AND event_type:Death.
Do NOT use +AND+ — the +-joined boolean form errors through these tools. Use a literal space around AND.
Multi-word values: join with + only for adjacency within a single field value (e.g. device.generic_name:infusion+pump). This is matched as tokens, not an exact phrase.
Avoid raw special characters ((, ), /, leading +) inside values — they break the query. Pick a simpler token (e.g. products.industry_name:Dietary instead of the full Dietary Conventional Foods/Meal Replacements).
Dates are strings: device AE/MAUDE use YYYYMMDD (e.g. date_received); recalls/enforcement use YYYY-MM-DD (e.g. event_date_initiated, recall_initiation_date).
Result envelope: every successful call returns {status:"success", data:{meta:{results:{total, skip, limit}}, results:[...]}}. Read the hit count from data.meta.results.total.
Counts/aggregations: native openFDA supports &count=<field>; these TU wrappers center on search. To rank terms, retrieve a batch (e.g. limit:100) and tally the field yourself in Python.
Interpretation Tables (raw openFDA field → meaning)
Medical devices — MAUDE adverse events (/device/event.json)
Field
Meaning
event_type
Death, Injury, Malfunction, or No answer provided. Death/Injury = patient harm; Malfunction = device failure without (reported) harm.
device[].generic_name / device[].brand_name
Device category / trade name.
device[].manufacturer_d_name
Device manufacturer.
patient[]
Patient-level outcome data (may be sparse).
mdr_text[].text
Narrative; text_type_code distinguishes event description vs manufacturer narrative.
report_number
MAUDE report id. Duplicate / follow-up reports of the same event are common — do not count reports as distinct events.
date_received
YYYYMMDD FDA received date.
Medical devices — recalls (/device/recall.json)
Field
Meaning
product_description
What was recalled.
recalling_firm
Firm issuing the recall.
recall_status
e.g. Open, Terminated. Terminated = FDA closed the action.
product_code
FDA device product code.
k_numbers[]
Associated 510(k) clearance numbers.
root_cause_description
FDA root-cause category (e.g. Labeling design).
event_date_initiated
YYYY-MM-DD recall start.
Enforcement reports (device / drug / food /.../enforcement.json)
Field
Meaning
classification
Recall severity: Class I (serious/fatal hazard), Class II (temporary/reversible), Class III (unlikely to cause harm).
e.g. Delay in shipping of the drug, Demand increase for the drug.
dosage_form
e.g. Injection, Tablet.
therapeutic_category[]
Clinical category.
company_name
Manufacturer.
update_type / initial_posting_date / update_date
Posting metadata.
Workflow
Classify the product from the request (device / food-supplement-cosmetic / vet / drug shortage / recall).
Pick the tool from the Tool Map.
Build the Lucene query following Query Grammar (single field for a first pass; add AND for combinations). Keep values simple; avoid special characters.
Run it and read data.meta.results.total and data.results[].
Interpret the fields with the table above. For severity: device event_type:Death; enforcement classification:Class I; CAERS outcomes:Death/Hospitalization; shortage status:Current.
Summarize and cite. Report counts, key fields, the query used, and the LIMITATIONS caveat. To rank terms, pull limit:100 and tally in Python (no count aggregation in these wrappers).
If out of scope (drug-AE signal/PRR/ROR), stop and route to the pharmacovigilance skills.
Worked Examples (verified live)
Example 1 — Device deaths for a device type (MAUDE)
"Are there any reported deaths in adverse-event reports for pacemakers?"
OpenFDA_search_device_adverse_events {"search":"device.generic_name:pacemaker AND event_type:Death","limit":1}
Real output (abbrev): status:success, meta.results.total = 16619; first record event_type = Death, device.generic_name = DEFIBRILLATOR/PACEMAKER.
Interpretation: 16,619 MAUDE reports match a pacemaker device with a Death event type. These are spontaneous reports — duplicates likely, and "Death" means a death was reported in temporal association, not that the device caused it.
Example 2 — Device recalls for a firm
"What device recalls has Medtronic Navigation issued?"
Real output (abbrev): total = 1896; first record recall_status = Terminated, product_code = HAW, root_cause_description = Labeling design, k_numbers = ["K990214"], event_date_initiated = 2011-01-20, product_description = a tactile probe for spine surgery.
Interpretation: 1,896 recall records match firms containing "Medtronic". recall_status: Terminated means FDA has closed this action; the root cause was a labeling-design issue.
Example 3 — Drug shortage lookup for an injectable
"Is ketorolac injection in shortage right now?"
OpenFDA_search_drug_shortages {"search":"dosage_form:Injection AND status:Current","limit":1}
Real output (abbrev): total = 799; first record generic_name = Ketorolac Tromethamine Injection, status = Current, shortage_reason = Delay in shipping of the drug, availability = Unavailable, company_name = Fresenius Kabi USA, LLC.
Interpretation: 799 current shortage records are injectables; ketorolac tromethamine injection is currently in shortage (status Current, availability Unavailable) due to a shipping delay.
Example 4 — Supplement / cosmetic CAERS reactions
"Are there CAERS adverse-event reports implicating cosmetics?"
Real output (abbrev): total = 52214; first record products[].industry_name = Cosmetics, products[].role = SUSPECT, outcomes = ["Hospitalization","Other Serious or Important Medical Event"].
Interpretation: 52,214 CAERS reports name a cosmetic product as SUSPECT. CAERS is voluntary; a SUSPECT role reflects the reporter's attribution, not a verified causal link.
Example 5 — Veterinary adverse events for a drug in a species
"What adverse events are reported for carprofen in dogs?"
OpenFDA_search_animalvet_adverse_events {"search":"drug.active_ingredients.name:carprofen AND animal.species:Dog","limit":1}
Real output (abbrev): total = 46469; first record animal.species = Dog, reaction[].veddra_term_name includes Leucocytosis NOS, Neutrophilia, Depression, Elevated alanine aminotransferase (ALT).
Interpretation: 46,469 veterinary reports match carprofen-containing products in dogs. VeDDRA terms describe reported clinical signs; counts reflect reporting, not incidence.
Limitations (state these in every report)
Spontaneous / voluntary reports. MAUDE (device), CAERS (food/supplement/cosmetic), FAERS (drug), and the animal/vet system are passive surveillance. Reports are unverified and a report is not a confirmed causal event.
No causation, no denominator. Counts (meta.results.total) are report counts, not incidence or rates. There is no exposure denominator, so you cannot compute risk.
Reporting bias. Serious events, new products, recalls, and media/regulatory attention drive reporting spikes (Weber effect). High counts may reflect usage volume or attention, not hazard.
Duplicate reports. MAUDE in particular contains follow-up/duplicate reports of the same event; do not treat report counts as event counts.
CAERS voluntary & sparse. Consumer/age/gender fields are often missing; SUSPECT role is reporter attribution.
This skill does not compute signal statistics (PRR/ROR/IC). For drug-AE disproportionality use tooluniverse-pharmacovigilance / tooluniverse-adverse-event-detection.
Data currency: each response carries a meta.last_updated date; openFDA lags real-world events.
See references/openfda_fields.md for the full per-endpoint field reference and additional query examples.
