Recall Radar

Check the baby gear you buy, borrow or inherit — and get walked through the official fix.

Recall Radar is an MCP server (Streamable HTTP, spec 2025-11-25) built for an Alexa+-style voice and screen assistant. A parent says "I'm buying a used Peg Perego high chair, can you check it for recalls?". Recall Radar works out what it needs to know (usually the model number and date on the label), reads the label from a typed line, a spoken line or a photo, decides whether a recall applies, shows how it decided, and then tracks the remedy until it's done.

Demo video (2:50): https://youtu.be/ra51u2v-cRU

Built for the Amazon Developer Hackathon, Alexa+ track. Alexa+ add-on tooling is partner-only, so the demo runs through a simulated Alexa+ host included in this repo (a real MCP client and MCP Apps host). Nothing in this repo is Amazon software. See Simulation.

Why it's built the way it is (the research)

Most "recall checker" demos look up a product name and show a list. That fails in practice, so before writing the matcher I measured the real data (scripts/coverage.ts, docs/results.txt):

What makes the matching trustworthy

• Language models may help read; code decides. The verdict compares explicit values: normalised model strings, parsed date spans, batch codes. Every verdict carries an evidence list (✓ Model matches, ✕ Made Nov 2021, outside the window on or after June 23, 2022).

• Near misses are caught. A one-character slip in a model number is reported as "very close to X, re-read the label", never silently accepted.

• Different-but-similar products are ruled out with a reason. "SnugRide" ≠ "SnugRide Turn & Slide"; one chair colour/model number ≠ another.

• A list that points elsewhere never clears a product. 59 baby-product notices say things like "visit the firm's website for the complete list". For those, an unlisted model becomes "check the full list", not "not recalled". This guard is measured (benchmark case J): with it disabled, 29 of 36 such recalls were wrongly ruled out; with it, 0.

• No "safe" claims. No match is reported as no match, with coverage (how many recalls, which years, data-through date) and a pointer to cpsc.gov / nhtsa.gov.

Related MCP server: RecallChecker

Results (self-generating benchmark)

npm run bench builds test cases from the real recalls (no hand-labelled set), then probes the engine, including deliberately wrong inputs:

A  full label -> CONFIRMED                            409/409   100.0%
B  model withheld -> never CONFIRMED                  409/409   100.0%
C  model typo -> not CONFIRMED                        409/409   100.0%
D  decoy model -> not CONFIRMED                       409/409   100.0%
E  date outside window -> not CONFIRMED                56/56    100.0%
F  brand+product only -> found in top 8               407/409    99.5%
G  car seat, date in window -> CONFIRMED               58/58    100.0%
H  car seat, date outside window -> not CONFIRMED      58/58    100.0%
I  no-identifier recall -> never CONFIRMED            538/538   100.0%
J  incomplete list + unlisted model -> never EXCLUDED  36/36    100.0%
K  date code not in list -> not CONFIRMED               9/9     100.0%
False-confirm rate across guard cases: 0/1924

Read these numbers honestly. The benchmark is generated from the same extractor it tests, so it proves the decision logic is consistent and guarded. It does not prove extraction recall on notices we haven't seen. So we also audit by hand, and every finding became a test:

• 30 random extractions checked by eye: about 26 fully correct; the 3 false positives (unit sizes like "10mL" read as models) were fixed.

• A second audit of notices that visibly name identifiers the extractor missed found 5 classes (connective words, date codes, model names, serial ranges, tables), now handled; one bug there was a regex that left a stray "s" after "numbers".

• Hand-testing real stories found three bugs the benchmark could not: an unrelated toy recall outranking a high chair, an old car-seat recall over-confirmed by a name that was merely a prefix, and the label parser reading "SnugRide" as serial "ugRide".

test/scenarios.test.ts pins those stories on the real data. Extraction coverage is in docs/results.txt: hard identifiers for 49% of notices (models 40%, manufacture windows 10%, batch codes 6%, date-code lists 4%, serial ranges 1%). For the other 51% the best the app can do is COMPARE_APPEARANCE.

Latency: a full check takes 3–15 ms locally (Alexa+ add-ons have a 500 ms round-trip budget).

Quick start

Requires Node 22+ (developed on 24). Optional: tesseract for label-photo OCR (brew install tesseract).

npm install
npm start            # MCP server + simulator on http://127.0.0.1:3333
npm test             # 58 tests, incl. real end-to-end scenarios and an in-memory MCP client
npm run bench        # generated benchmark
npm run sync         # refresh the CPSC + NHTSA snapshots (data/*.json)

Open http://127.0.0.1:3333/ and use the suggested prompts, or type.

The repo ships with a snapshot of the data (1,028 baby/children's CPSC recalls and 177 NHTSA child-seat records, 2012 to Oct 2026, both U.S. public-domain government data), so it runs offline. npm run sync rebuilds it from the live sources.

Use it from any MCP client

claude mcp add --transport http recall-radar http://127.0.0.1:3333/mcp

(Or any client that speaks Streamable HTTP. The server is stateless and answers with JSON.)

Tools

Few, outcome-oriented tools, each answering a whole intent, with a speakable headline and a nextQuestion so an agentic host can keep the conversation moving.

Results include structuredContent plus an MCP App (ui://recall-radar/result, _meta.ui.resourceUri) designed for an Echo Show read at arm's length, in one 1280×800 screen with no scrolling for the key facts:

• a large status hero (recalled / one more detail / compare by eye / no match / not covered). Colours never use "green = safe": a ruled-out item is neutral, because other recalls may still exist;

• a comparison table, the centrepiece: for each requirement (model, date made, date code, batch, serial) what the recall says vs. what you told me, with ✓ / ✕ / ≈ / ? states, so the reason for a verdict is visible at a glance;

• the recalled product's own label photo ("where to look"), the official fix and contact, and a progress timeline for tracking the remedy;

• dark/light themes, 52px touch targets, focus rings, reduced-motion support, and a loading skeleton via ui/notifications/tool-input.

Simulation

The hackathon FAQ states that the Alexa+ MCP toolkit, CLI and web simulator are available to select partners only, and that a web page acting as a real MCP client is the accepted path. simulator/ is exactly that:

• a real MCP client: initialize (declaring protocol 2025-03-26, as Alexa+'s client does), tools/list, resources/read, tools/call over Streamable HTTP;

• an MCP Apps host: renders the ui:// resource in a sandboxed iframe (allow-scripts only) and bridges JSON-RPC over postMessage, including the app calling tools back (update_case);

• a thin rule-based "Alexa" that routes utterances to tools. It is deliberately small; all recall logic lives in the server, so a real LLM host would replace only this router;

• voice in/out via the browser's Web Speech API, label-photo upload via local Tesseract OCR, and a live protocol drawer ("Under the hood") with the 500 ms latency budget;

• ▶ Play demo types and runs the whole story hands-free (for recording), and Reset clears the conversation and saved items.

Tested with a real AI assistant

Besides the simulator, we connected Claude Code (a real MCP-speaking assistant) to the server with built-in tools disabled and gave it plain-English requests. It found and used the right tool on its own, asked for the exact missing detail, and found a real bug (a fuzzy saved-item name merged one item's model number into another item's check; now fixed and pinned by tests). Transcripts and the reproduction command are in docs/REAL_ASSISTANT_RUNS.md.

The reusable part: mcp-apps-sim

The simulator's generic half (an MCP client, an MCP Apps host, pre-flight checks and tool forms) is published as its own open-source project, usable with any MCP server: https://github.com/muhammedidris004/mcp-apps-sim. For example, to run its pre-flight checks and app renderer against this server:

npm start                                          # Recall Radar on :3333
npx github:muhammedidris004/mcp-apps-sim http://127.0.0.1:3333/mcp

Architecture

simulator/ (MCP client + MCP Apps host)         any MCP client (Alexa+, Claude, …)
        │  Streamable HTTP  POST /mcp                    │
        ▼                                                ▼
src/http.ts ──► src/server.ts (5 tools + ui:// resource)
                  ├─ src/label.ts     label text → brand/model/date/batch   (+ Tesseract OCR route)
                  ├─ src/engine.ts    search → assess → rank → coverage
                  │    ├─ src/search.ts   BM25 + synonyms + brand/type gates + model-number boost
                  │    └─ src/verdict.ts  evidence-based decision (model, date window, batch, retail context)
                  ├─ src/cases.ts     case memory (resume / track the remedy)
                  └─ src/ingest.ts ► src/extract.ts   prose → structured conditions (CPSC), structured (NHTSA)
data/cpsc.json, data/nhtsa.json  (from scripts/sync-data.ts)

Limits (what this does not do)

• Pull, not push. It answers when asked. As far as the Alexa+ docs we could access say, an add-on can't make Alexa speak up unprompted, so "your toaster was just recalled" isn't possible.

• U.S. data only (CPSC, NHTSA). Not Health Canada, EU Safety Gate, or FDA food/drug.

• Not exhaustive. Recalls without model numbers can only be matched by appearance. Some notices list models on the manufacturer's site; we can't see those. "No match" is never "safe".

• Rule-based extraction. An LLM could cover the long tail of phrasing; ConditionExtractor in src/extract.ts is the seam for that. Not implemented, because it would add cost and make results less auditable.

• OCR quality depends on the photo. It's local Tesseract; for hard labels the user can read the details aloud.

• Alexa+ certification rules (privacy policy, no children's data, etc.) were reviewed but not exercised, since the add-on can't be submitted from outside the partner program.

Layout

src/        server, engine, extraction, label parser, case store, MCP App (src/ui/result.html)
simulator/  MCP client + MCP Apps host (open source mini-challenge deliverable: reusable test harness)
test/       unit + end-to-end scenario tests
bench/      generated benchmark
scripts/    sync-data.ts, coverage.ts
docs/       results.txt, DEMO_SCRIPT.md, FRICTION_LOG.md

Credits

Narration voice: Kokoro-82M (Apache-2.0), run locally; see docs/VOICE.md.

License

MIT. Recall data is from U.S. government sources (CPSC, NHTSA) and is public domain; always confirm with the official notices.