Name: 483 Risk Radar by Health AI
Author: radar.healthai.com

483 Risk Radar by Health AI MCP

FDA device & vehicle recall risk for AI agents: recall history, MAUDE trend, risk score.

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What it does

FDA device & vehicle recall risk for AI agents: recall history, MAUDE trend, risk score.

Quickstart

# 1 — add it to Claude Code
claude mcp add --scope user --transport http 483-risk-radar-by-health-ai-mcp https://radar.healthai.com/api/mcp

# 2 — ask your agent for something these tools do
#   device_risk_lookup, firm_compliance_history, watchlist_diff and 11 more

483 Risk Radar by Health AI costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.

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Tool surface

What the model actually sees. Descriptions are diffed on every release — see version history.

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Review a medical-device category's public FDA signals by three-letter product code (e.g. FRN = infusion pump). Returns recalls, MAUDE adverse-event trend, warning-letter matches, a normalized category signal, its driver contributions, and interpretation limits. It does not predict enforcement against a firm.

Parameter	Type	Required	Notes
product_code	string	yes	FDA product code, e.g. FRN

Build a recent, source-bounded FDA public-record timeline for a device firm: matched recalls, warning letters, and Form 483 citations where exact FEI numbers are available. Product codes are discovered from Constat's AI/ML-device corpus or may be supplied explicitly. Returns attribution and coverage limits with the records; it is not a finding of noncompliance or a prediction of FDA action.

Parameter	Type	Required	Notes
firm_name	string	yes	FDA applicant or company name, e.g. 'Medtronic'
product_codes	array	no	Optional FDA product-code scope. When omitted, Constat discovers codes from applicant matches in its device corpus.
fei_numbers	array	no	Optional exact FDA FEI numbers; improves Form 483 attribution.
since	string	no	Earliest event date, YYYY-MM-DD. Defaults to five years ago.
limit	integer	no	Maximum records per source and events in the timeline (default 20).

Return machine-generated FDA public-record changes detected for monitored product codes since a caller-supplied date, plus each code's latest category snapshot and postmarket coverage. Defaults to Constat Radar's five-code watchlist and the last seven days. Analyst verdict text and internal review status are excluded; use next_since as the next polling cursor.

Parameter	Type	Required	Notes
product_codes	array	no	FDA product codes to poll. Omit for the default Radar watchlist.
since	string	no	Return changes on or after this ISO date or UTC timestamp. Defaults to the last seven days; pass the prior response's next_since for exact polling.
limit	integer	no	Maximum changes to return (default 10).

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.

Parameter	Type	Required	Notes
k_number	string	yes	510(k) or De Novo number, e.g. K252148 or DEN180001

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 diff

Parameter	Type	Required	Notes
product_code	string	no	FDA product code, e.g. QAS
panel	string	no	Advisory panel, e.g. Radiology
applicant	string	no	Substring match on applicant/company name
has_clinical_data	boolean	no	—
reports_any_sensitivity_metric	boolean	no	Reported any sensitivity metric (canonical — includes per-finding sensitivities, not just the top-level slot). Not a claim of cross-device comparability.
has_pccp	boolean	no	Included a Predetermined Change Control Plan
limit	integer	no	—
cursor	string	no	Opaque continuation cursor returned by a prior page.

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.

Parameter	Type	Required	Notes
k_number	string	yes	510(k) or De Novo number, e.g. K252148 or DEN180001
depth	integer	no	Max ancestry depth (default 4)

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 (median years between a clearance and its cited predicates) is included when decision-d

Parameter	Type	Required	Notes
panel	string	no	Advisory panel, e.g. Radiology; omit for all

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 judgmen

Parameter	Type	Required	Notes
k_number	string	yes	510(k) or De Novo number, e.g. K252148 or DEN180001

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.

Parameter	Type	Required	Notes
product_code	string	no	FDA product code, e.g. QIH
panel	string	no	Advisory panel, e.g. Radiology
applicant	string	no	Substring match on applicant/company name
has_drift_signal	boolean	no	—
signal_specificity	string	no	Restrict the drift-signal filter to one tier: 'device' = attributable to this device (own line re-cleared, own applicant's recall, predicate neighbors) — the high-signal tier; 'product_code' = observe
min_recalls_24mo	integer	no	Product-code recalls in trailing 24 months
maude_trend_up	boolean	no	Code-level MAUDE trend above its own 3-year baseline
limit	integer	no	—
cursor	string	no	Opaque continuation cursor returned by a prior page.

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.

Parameter	Type	Required	Notes
panel	string	no	Advisory panel, e.g. Radiology; omit for all

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 mu

Parameter	Type	Required	Notes
k_number	string	no	FDA clearance number, e.g. DEN170073 or K252148
cpt_code	string	no	Bare CPT code, e.g. 75580 or 0932T

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).

Parameter	Type	Required	Notes
mechanism	string	no	Payment mechanism
cpt_category	string	no	CPT category
ntap_status	string	no	—
applicant	string	no	Substring match on device maker / applicant name
has_cms_rate	boolean	no	Only pathways with a known CMS dollar rate
limit	integer	no	—
cursor	string	no	Opaque continuation cursor returned by a prior page.

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.

No parameter schema on file.

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).

Parameter	Type	Required	Notes
make	string	yes	e.g. honda
model	string	yes	e.g. civic
year	string	yes	model year, e.g. 2020

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Where are you running it?

Tool surface

Release history

483 Risk Radar by Health AI MCP

What it does

Quickstart

Where are you running it?

Tool surface

What counts against your monthly calls

Release history

See also