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FDA medical-device regulatory intelligence from keyless openFDA datasets.
Strong foundation with 10 well-named tools, comprehensive descriptions, and documented schemas. All tools follow verb_noun naming (search_, build_). Descriptions are detailed and include important regulatory disclaimers (e.g., '510(k) clearance is not FDA approval'). Input schemas are complete with type definitions and parameter descriptions. Output schemas are defined for all tools. However, several parameters lack explicit constraints (enums, min/max), and error handling guidance is absent. No tool annotations (readOnlyHint, destructiveHint) despite all being read-only operations. Parameter descriptions could be more prescriptive about format/range expectations.
Search FDA 510(k) premarket notifications by device, applicant, product code, or K number. Clearance means FDA found substantial equivalence; it is not an FDA approval or endorsement.
Search FDA MAUDE medical-device reports by manufacturer, brand/device, product code, event type, or PMA/510(k) number. MAUDE reports are unverified signals: they cannot establish causation, incidence, prevalence, or comparative safety.
Look up FDA device classification and regulatory context by product code, device name, or regulation number. Classification describes the product-code category, not a specific product's clearance or approval.
Build a bounded FDA regulatory snapshot for one device company across 510(k), PMA, recalls, and MAUDE. Dataset name matching is imperfect and MAUDE counts are signals, not safety rates.
Search FDA device registration/listing data by firm, registration number, product code, or listing number. Registration/listing does not mean FDA approval, clearance, certification, or endorsement.
Missing tool annotations (readOnlyHint) despite all 10 tools being read-only. Agents cannot distinguish safe tools from destructive ones without explicit hints.
Parameter 'limit' lacks explicit min/max constraints in descriptions. Baseline rubric requires 'Results (1-100, default 20)' format; most tools omit the range. LLMs may pass invalid values.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 62 | 2025-06-18+ | v2 |
Aggregate MAUDE reports for a device query by event type, manufacturer, product code, or receive date. Counts reflect reporting and database artifacts—not event rates or causal risk—and must not be compared without exposure denominators.
Search FDA Premarket Approval (PMA) decisions and supplements by trade/generic name, applicant, product code, or PMA number. Supplements may represent manufacturing or labeling changes rather than new devices.
Build a bounded cross-dataset snapshot for one FDA product code: classification, recent 510(k)s, PMAs, recalls, and MAUDE reports. Dataset counts have different meanings; MAUDE counts are not event rates.
Search FDA medical-device recall records by firm, product, product code, K number, status, or date. A recall record describes a correction/removal action and does not by itself establish patient harm.
Search FDA GUDID/UDI records by brand, company, device identifier, or product code. A UDI record describes an identified device in GUDID; it does not establish current sales, availability, clearance, approval, or safety.
No error handling guidance in tool descriptions. If a search returns zero results or an invalid product_code is passed, the LLM has no recovery path documented.
Parameter 'event_type' in fda_device_adverse_events lacks enum constraint. Description says 'Death, Injury, Malfunction, or Other' but no enum field enforces it. LLMs may hallucinate invalid values.
fda_device_classification has three optional parameters (product_code, device, regulation_number) with no guidance on which to prefer or whether they are mutually exclusive. LLMs may pass all three or none.