Fda risk radar MCP agent
Constat — FDA & NHTSA regulatory-risk MCP server for AI agents.
by thehealthai·MIT license·★ 0 Stars on the repo·GitHub ↗
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Constat — 483 Risk Radar (MCP server)
The MCP server behind Constat — FDA & NHTSA regulatory-risk intelligence for AI agents, over the Model Context Protocol. Live public regulatory data — recalls, adverse events, warning letters, 510(k) premarket evidence, postmarket drift signals, reimbursement pathways, and vehicle safety. Decision support, not regulatory advice.
- Endpoint:
https://constat.dev/api/mcp(legacyhttps://radar.healthai.com/api/mcpstill serves) - Transport: Streamable HTTP (JSON-RPC 2.0)
- Auth: none for anonymous access (10 work units/min and 50/day per
anonymous source); an optional
X-API-Keyheader enables the separately assigned metered allowance. Tool calls have documented work-unit costs. - Registry:
com.healthai/radar(official MCP registry)
The evidence corpus and tool logic run hosted at the endpoint above. This repo
also ships server.mjs, a zero-dependency stdio bridge to that endpoint, so
stdio-only MCP clients can use the server like any local one:
node server.mjs # stdio MCP server, bridges to constat.dev/api/mcp
// e.g. in an MCP client config
{ "mcpServers": { "constat": { "command": "node", "args": ["/path/to/server.mjs"] } } }
docker build -t constat-mcp . && docker run -i constat-mcp # same, containerized
Tools
| Tool | What it does |
|---|---|
device_risk_lookup |
FDA compliance risk for a device category by 3-letter product code — recalls, MAUDE trend, warning-letter matches, composite score |
firm_compliance_history |
Source-bounded FDA public-record timeline for a device firm — recalls, warning letters, 483s, clearances |
watchlist_diff |
Machine-detected FDA public-record changes for monitored product codes since a given date |
device_evidence_lookup |
Parsed 510(k) premarket evidence for an AI/ML device, each field with a verbatim source quote + page |
evidence_search |
Find AI/ML clearances by product code, panel, applicant, clinical data, sensitivity metric, or PCCP |
predicate_chain |
Trace a device's predicate ancestry with each predicate's age at clearance |
evidence_cohort_stats |
Reporting-rate stats across the parsed AI/ML corpus — presence figures with denominators |
device_postmarket_lookup |
Post-clearance intelligence for one device — recalls, MAUDE trend, letter/483 matches, drift signals |
postmarket_search |
Find devices by postmarket criteria — drift signals, recalls in 24mo, rising MAUDE trend |
cohort_postmarket_stats |
Postmarket presence rates across the AI/ML cohort, each with its denominator |
reimbursement_lookup |
Clearance-to-payment pathway by K/DEN or CPT code — NTAP, Cat I/III, CMS rates, HCPCS, LCDs |
reimbursement_search |
Find payment pathways by mechanism, CPT category, NTAP status, applicant |
reimbursement_stats |
Mechanism distribution across the reimbursement corpus with dollar ranges |
vehicle_risk_lookup |
NHTSA safety history by make/model/year — recall campaigns and complaint stats |
Quick start
curl -s https://constat.dev/api/mcp \
-H 'content-type: application/json' \
-H 'accept: application/json, text/event-stream' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call",
"params":{"name":"device_risk_lookup","arguments":{"product_code":"FRN"}}}'
List tools with {"method":"tools/list"}. Note the Accept header must include
text/event-stream (streamable-HTTP requirement).
Each of the 14 tools declares an output schema. Tool calls return structured
content with an explicit ok, not_found, invalid_request, or unavailable
status while preserving a text result for older clients.
The tools are observational and non-destructive, but advertise
readOnlyHint: false because calls write bounded quota and operational-
telemetry state. They do not publish or modify FDA, CMS, NHTSA, or other
third-party records.
See the integration guide, architecture and trust boundaries, privacy notice, and support page.
Related servers
- Clarity MCP — condition-aware ingredient, product & supplement safety (verdict + evidence tier + citation).
- MCP Queen — the graded MCP registry that independently probes and grades this server.
About
Built by Health AI, the team behind Constat. Informational only; verify against primary FDA/NHTSA sources.
License
MIT — see LICENSE.
| 1 | # Constat — 483 Risk Radar (MCP server) |
| 2 | |
| 3 | [![Constat MCP — FDA Device Evidence Lifecycle MCP connector – tool definition quality and endpoint health on Glama]](https://glama.ai/mcp/connectors/com.healthai/radar) |
| 4 | |
| 5 | <a href="https://glama.ai/mcp/servers/@thehealthai/fda-risk-radar-mcp"> |
| 6 | <img width="380" height="200" src="https://glama.ai/mcp/servers/@thehealthai/fda-risk-radar-mcp/badge" alt="Constat 483 Risk Radar MCP server" /> |
| 7 | </a> |
| 8 | |
| 9 | [![MCP Queen grade]](https://mcpqueen.com/s/com.healthai/radar) |
| 10 | |
| 11 | The MCP server behind [**Constat**] — FDA & NHTSA |
| 12 | regulatory-risk intelligence for AI agents, over the Model Context Protocol. |
| 13 | Live public regulatory data — recalls, adverse events, warning letters, 510(k) |
| 14 | premarket evidence, postmarket drift signals, reimbursement pathways, and |
| 15 | vehicle safety. **Decision support, not regulatory advice.** |
| 16 | |
| 17 | **Endpoint:** `https://constat.dev/api/mcp` (legacy `https://radar.healthai.com/api/mcp` still serves) |
| 18 | **Transport:** Streamable HTTP (JSON-RPC 2.0) |
| 19 | **Auth:** none for anonymous access (10 work units/min and 50/day per |
| 20 | anonymous source); an optional `X-API-Key` header enables the separately |
| 21 | assigned metered allowance. Tool calls have documented work-unit costs. |
| 22 | **Registry:** [`com.healthai/radar`] (official MCP registry) |
| 23 | |
| 24 | The evidence corpus and tool logic run hosted at the endpoint above. This repo |
| 25 | also ships `server.mjs`, a zero-dependency **stdio bridge** to that endpoint, so |
| 26 | stdio-only MCP clients can use the server like any local one: |
| 27 | |
| 28 | |
| 29 | node server.mjs # stdio MCP server, bridges to constat.dev/api/mcp |
| 30 | |
| 31 | |
| 32 | |
| 33 | // e.g. in an MCP client config |
| 34 | { "mcpServers": { "constat": { "command": "node", "args": ["/path/to/server.mjs"] } } } |
| 35 | |
| 36 | |
| 37 | |
| 38 | docker build -t constat-mcp . && docker run -i constat-mcp # same, containerized |
| 39 | |
| 40 | |
| 41 | ## Tools |
| 42 | |
| 43 | | Tool | What it does | |
| 44 | |------|--------------| |
| 45 | | `device_risk_lookup` | FDA compliance risk for a device category by 3-letter product code — recalls, MAUDE trend, warning-letter matches, composite score | |
| 46 | | `firm_compliance_history` | Source-bounded FDA public-record timeline for a device firm — recalls, warning letters, 483s, clearances | |
| 47 | | `watchlist_diff` | Machine-detected FDA public-record changes for monitored product codes since a given date | |
| 48 | | `device_evidence_lookup` | Parsed 510(k) premarket evidence for an AI/ML device, each field with a verbatim source quote + page | |
| 49 | | `evidence_search` | Find AI/ML clearances by product code, panel, applicant, clinical data, sensitivity metric, or PCCP | |
| 50 | | `predicate_chain` | Trace a device's predicate ancestry with each predicate's age at clearance | |
| 51 | | `evidence_cohort_stats` | Reporting-rate stats across the parsed AI/ML corpus — presence figures with denominators | |
| 52 | | `device_postmarket_lookup` | Post-clearance intelligence for one device — recalls, MAUDE trend, letter/483 matches, drift signals | |
| 53 | | `postmarket_search` | Find devices by postmarket criteria — drift signals, recalls in 24mo, rising MAUDE trend | |
| 54 | | `cohort_postmarket_stats` | Postmarket presence rates across the AI/ML cohort, each with its denominator | |
| 55 | | `reimbursement_lookup` | Clearance-to-payment pathway by K/DEN or CPT code — NTAP, Cat I/III, CMS rates, HCPCS, LCDs | |
| 56 | | `reimbursement_search` | Find payment pathways by mechanism, CPT category, NTAP status, applicant | |
| 57 | | `reimbursement_stats` | Mechanism distribution across the reimbursement corpus with dollar ranges | |
| 58 | | `vehicle_risk_lookup` | NHTSA safety history by make/model/year — recall campaigns and complaint stats | |
| 59 | |
| 60 | ## Quick start |
| 61 | |
| 62 | |
| 63 | curl -s https://constat.dev/api/mcp \ |
| 64 | -H 'content-type: application/json' \ |
| 65 | -H 'accept: application/json, text/event-stream' \ |
| 66 | -d '{"jsonrpc":"2.0","id":1,"method":"tools/call", |
| 67 | "params":{"name":"device_risk_lookup","arguments":{"product_code":"FRN"}}}' |
| 68 | |
| 69 | |
| 70 | List tools with `{"method":"tools/list"}`. Note the `Accept` header must include |
| 71 | `text/event-stream` (streamable-HTTP requirement). |
| 72 | |
| 73 | Each of the 14 tools declares an output schema. Tool calls return structured |
| 74 | content with an explicit `ok`, `not_found`, `invalid_request`, or `unavailable` |
| 75 | status while preserving a text result for older clients. |
| 76 | |
| 77 | The tools are observational and non-destructive, but advertise |
| 78 | `readOnlyHint: false` because calls write bounded quota and operational- |
| 79 | telemetry state. They do not publish or modify FDA, CMS, NHTSA, or other |
| 80 | third-party records. |
| 81 | |
| 82 | See the [integration guide], [architecture |
| 83 | and trust boundaries](https://constat.dev/architecture), [privacy |
| 84 | notice](https://constat.dev/privacy), and [support page]. |
| 85 | |
| 86 | ## Related servers |
| 87 | |
| 88 | [Clarity MCP] — condition-aware |
| 89 | ingredient, product & supplement safety (verdict + evidence tier + citation). |
| 90 | [MCP Queen] — the graded MCP registry |
| 91 | that independently probes and grades this server. |
| 92 | |
| 93 | ## About |
| 94 | |
| 95 | Built by [Health AI], the team behind |
| 96 | [Constat]. Informational only; verify against primary |
| 97 | FDA/NHTSA sources. |
| 98 | |
| 99 | ## License |
| 100 | |
| 101 | MIT — see [LICENSE]. |
| 102 |
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