servers / 483-risk-radar-by-health-ai
483 Risk Radar by Health AI MCP server
communitystreamable_httpremotewrite capablehealthy
FDA device & vehicle recall risk for AI agents: recall history, MAUDE trend, risk score.
01Tools · 12
How to read this: tool names here are observed from a live tools/list handshake. The Risk label is a heuristic inferred from the tool name (write/destructive verbs), not from executing the tool — a conservative guess, not a verified capability. We never escalate risk from a description. Found one that's wrong? Tell us — we fix on report.
| Tool | Risk | Side effects | Approval |
|---|---|---|---|
| device_postmarket_lookup Post-clearance intelligence for one AI/ML device by 510(k) number: its product code's recalls, MAUDE adverse-event level and trend, warning-letter and 483 matches for the applicant, plus per-device drift signals (adverse-event inflection, re-clearances of the same device line, software-recall patterns, predicate-cohort recall activity). Descriptive observables with sources — never a safety judgment. | write | true | unknown |
| evidence_search Find AI/ML device clearances by filter — product code, panel, applicant, and whether the submission reported clinical data, any sensitivity metric, or a PCCP. Answers 'what evidence did FDA accept for devices like mine'. Returns matching records with their parsed evidence. Presence flags are descriptive: 'reports a sensitivity metric' is not 'reports a comparable sensitivity' — analysis units differ across devices. | read | false | unknown |
| postmarket_search Find AI/ML devices by postmarket criteria — product code, panel, applicant, whether any drift signal exists, minimum recalls in 24 months, or a rising MAUDE trend. Returns per-device postmarket summaries with drift-signal counts. | read | false | unknown |
| reimbursement_stats Distribution of payment mechanisms across the AI/ML reimbursement corpus — pathway and distinct-device counts per mechanism (NTAP, Cat I, Cat III/APC, …) with the min/median/max dollar amounts for each. Deliberately never a single pooled 'reimbursement rate': NTAP add-on amounts and CMS rates are different measurements and are reported separately with their own spreads. | write | true | unknown |
| cohort_postmarket_stats Postmarket presence rates across the snapshotted AI/ML device cohort (optionally by panel): share with any recall in 24 months, with a rising MAUDE trend, with any drift signal, with a warning-letter match — every rate with its denominator inline, never pooled across devices. | unknown | unknown | unknown |
| reimbursement_lookup Trace the clearance-to-payment pathway for an AI/ML device by FDA clearance number (K/DEN, e.g. DEN170073) OR bare CPT code (e.g. 75580). Returns every payment mechanism (NTAP add-on, Category I/III CPT + CMS rate, HCPCS, MAC LCD) with amounts, effective dates, and source links, plus any commercial/MAC payer coverage policies that reference the clearance or its codes. Answers 'who got paid, how much, through which mechanism, on what basis.' CPT codes are bare factual identifiers only — no procedure descriptors; follow the CMS source link for the official descriptor. | write | true | unknown |
| reimbursement_search Find AI/ML device payment pathways by mechanism — e.g. 'devices that got NTAP', 'devices paid under a Category I CPT code', 'pathways with a known CMS dollar rate'. Filters: mechanism, CPT category, NTAP status, applicant. Returns pathways with amounts, effective dates, and sources. Use reimbursement_stats for the mechanism distribution (never a single pooled reimbursement rate). | read | false | unknown |
| device_risk_lookup Look up FDA compliance risk for a medical device category by three-letter product code (e.g. FRN = infusion pump). Returns recall history, MAUDE adverse-event trend, warning-letter matches, and a composite risk score. | unknown | unknown | unknown |
| device_evidence_lookup Look up the structured premarket evidence FDA accepted for a specific AI/ML-enabled device by 510(k) number (e.g. K252148). Returns parsed summary fields — validation study design, sample sizes, endpoints, reported performance, predicate chain, PCCP — each with a verbatim source quote and page. Null means the summary did not state it. | unknown | unknown | unknown |
| predicate_chain Trace the predicate ancestry of a 510(k) device, with each cited predicate's age (how many years old the predicate was when the child cleared). Reveals how AI/ML devices chain to older predicates. | unknown | unknown | unknown |
| evidence_cohort_stats Reporting-rate stats across the parsed AI/ML corpus (optionally by panel). Each rate is a presence figure with its denominator — 'reported in X of Y audited devices' — never a pooled performance value. Excludes not-yet-parsed devices from every denominator and discloses the parse queue separately. Predicate age is reported as unavailable pending a data audit (corpus is recency-biased). | unknown | unknown | unknown |
| vehicle_risk_lookup Look up NHTSA safety history for a vehicle by make, model, and model year. Returns recall campaigns and complaint statistics (crashes, fires, injuries, top components). | unknown | unknown | unknown |
02Install & source
https://radar.healthai.com/api/mcp
remote_url03Access granted
Knowledge & memory · writeProcess payments · write
The access this server can exercise, inferred from its verified tools — not a declared OAuth scope.
05Provenance & freshness
sourcesOfficial MCP Registry [p1]
last_checked2026-07-10 02:51Z
next_check2026-07-12 02:51Z
cadenceevery 48h
verifiedtools_list:passed handshake:passed metadata:failed tools_list:passed handshake:passed metadata:failed
index_statusindex — 5 unique facts >= 5
06Badge
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