# US Restaurant Health Inspection Scores (`filingradar/us-restaurant-inspections`) Actor
Restaurant & food-establishment health inspections from public city open-data (NYC + Chicago), normalized to one schema with result, letter grade, score, critical flags, and geocode. One row per inspection. Public data only.
- **URL**: https://apify.com/filingradar/us-restaurant-inspections.md
- **Developed by:** [Filing Radar](https://apify.com/filingradar) (community)
- **Categories:** Business, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
Pay per usage
This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## US Restaurant Health Inspection Scores
**Every restaurant inspection, normalized to one schema.** This Actor pulls **health-inspection results** from official city open-data portals — with **result, letter grade, score, critical-violation flags, and geocode** — one clean row per inspection, across cities that each report it differently. Flags the **recent ones** (last 30 days).
> **Live coverage: New York City + Chicago.** More cities on request. 100% public government data — no logins, no gray areas.
---
### Who uses this
- **Food-delivery & reservation platforms** — surface or screen on safety; flag failing venues.
- **Restaurant & hospitality insurers, underwriters** — risk-score a venue before binding.
- **Franchise & multi-unit QA** — monitor every location's inspections in one feed.
- **Food-safety SaaS, proptech, market researchers** — a normalized cross-city score nobody else publishes.
- **Real estate** — failing/closing restaurants signal turnover.
### What's in every record (one row per inspection)
| Field | Example |
|-------|---------|
| `name` / `cuisine_or_type` | `Mona Lisa Pizzeria & Ristorante` / `Pizza` |
| `result` | `pass` · `conditional` · `fail` · `closed` |
| `grade` / `score` | `A`/`B`/`C` (NYC) · `22` |
| `critical_flag` | `true` |
| `violation_summary` | `Evidence of mice in establishment's food areas` |
| `inspection_date` / `inspection_type` | `2026-06-15` / `Cycle Inspection` |
| `risk` | `Risk 1 (High)` (Chicago) |
| `address` · `city` · `county` · `state` · `zip` | `839 Annadale Rd`, `Staten Island`, `NY` |
| `lat` / `lng` | geocoded where the city provides it |
| `change_type` | **`NEW`** = inspected in the last 30 days |
Export to **JSON, CSV, or Excel**, or pull via the **Apify API** into your app.
### Inputs
| Input | What it does |
|-------|--------------|
| **Cities / states** | `NY` (NYC), `IL` (Chicago). |
| **Filter** | Empty = all. `["NEW"]` = only inspections in the last 30 days. |
| **Max records per city** | Cap the volume (and your cost) per run. |
### Pricing
**Pay per result** — you only pay for the records you receive: **$0.004 per delivered inspection record.** No subscription, no minimums.
### Data sources & legality
100% **public** city open-data APIs, intended for reuse:
- **NYC** — DOHMH Restaurant Inspection Results (`data.cityofnewyork.us`)
- **Chicago** — Food Inspections (`data.cityofchicago.org`)
Public establishment-level inspection records — no personal data. The Actor only touches public endpoints, never logged-in or ToS-gated sources, and rate-limits politely.
### FAQ
**How fresh is it?** As fresh as the city portals — `NEW` flags any inspection within the last 30 days.
**Which cities?** NYC + Chicago now. Want LA, Austin, Seattle, or your city next? Request it.
**Is this AI-generated?** The data is real government open-data; an automated pipeline normalizes and de-duplicates it. No fabricated rows, ever.
**One row per inspection?** Yes — NYC reports one row per violation; we collapse to one row per inspection with the grade/score/result.
---
**For developers / source:** zero-dependency Node 20 (built-in `fetch`). `node src/run.mjs` runs the pipeline locally. Public-data only; normalization is deterministic.
# Actor input Schema
## `cities` (type: `array`):
Which to include. Supported: NY (New York City), IL (Chicago). More on request.
## `change_types` (type: `array`):
Empty = all inspections. ["NEW"] = only inspections in the last 30 days.
## `limit` (type: `integer`):
Upper bound on rows pulled per city per run.
## Actor input object example
```json
{
"cities": [
"NY",
"IL"
],
"change_types": [],
"limit": 1000
}
````
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {};
// Run the Actor and wait for it to finish
const run = await client.actor("filingradar/us-restaurant-inspections").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {}
# Run the Actor and wait for it to finish
run = client.actor("filingradar/us-restaurant-inspections").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{}' |
apify call filingradar/us-restaurant-inspections --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=filingradar/us-restaurant-inspections",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "US Restaurant Health Inspection Scores",
"description": "Restaurant & food-establishment health inspections from public city open-data (NYC + Chicago), normalized to one schema with result, letter grade, score, critical flags, and geocode. One row per inspection. Public data only.",
"version": "1.0",
"x-build-id": "XfZqpXt3ghnGavmTQ"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/filingradar~us-restaurant-inspections/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-filingradar-us-restaurant-inspections",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/filingradar~us-restaurant-inspections/runs": {
"post": {
"operationId": "runs-sync-filingradar-us-restaurant-inspections",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/filingradar~us-restaurant-inspections/run-sync": {
"post": {
"operationId": "run-sync-filingradar-us-restaurant-inspections",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"cities": {
"title": "Cities / states",
"type": "array",
"description": "Which to include. Supported: NY (New York City), IL (Chicago). More on request.",
"items": {
"type": "string",
"enum": [
"NY",
"IL"
]
},
"default": [
"NY",
"IL"
]
},
"change_types": {
"title": "Filter",
"type": "array",
"description": "Empty = all inspections. [\"NEW\"] = only inspections in the last 30 days.",
"items": {
"type": "string",
"enum": [
"NEW",
"UNCHANGED"
]
},
"default": []
},
"limit": {
"title": "Max records per city",
"minimum": 1,
"maximum": 50000,
"type": "integer",
"description": "Upper bound on rows pulled per city per run.",
"default": 1000
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```