# Restaurant Health Inspections (New York City & Austin) (`lepidata/restaurant-health-inspections`) Actor

Food establishment inspection scores, grades and violations for New York City and Austin, as each city publishes them, with per-row source and version. Unofficial.

- **URL**: https://apify.com/lepidata/restaurant-health-inspections.md
- **Developed by:** [Lepi Data](https://apify.com/lepidata) (community)
- **Categories:** Business, Lead generation
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.10 / 1,000 inspection or facility records

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Restaurant Health Inspections (New York City & Austin)

Food establishment inspection scores, grades and violations for New York City and Austin, as each city publishes them, with per-row source and version. Unofficial.

**Modes** (`endpoint`)

- `inspections`: Search inspections (set filters.city); pages automatically up to Max results
- `nyc-facility`: NYC facility history (ids: NYC CAMIS numbers)
- `austin-facility`: Austin facility history (ids: Austin facility IDs)

**Filters** are passed as JSON in `filters`, using the same names as the output's `query` object (e.g. {"city":"nyc","zip":"10001"}).

**Output.** One row per record (`type: "record"`), plus `source_notice` and `removal` on every row. Free status rows (never charged): `invalid-input`, `not-found`, `no-results`, `api-error`, `service-busy`, `run-limit-reached`, `free-plan-limit`, `charge-limit-reached`.

Example row:

```json
{
 "city": "nyc",
 "facility_id": "50103573",
 "camis": "50103573",
 "name": "LAVAN 541",
 "address": {
  "building": "541",
  "street": "WEST 25 STREET",
  "borough": "Manhattan",
  "zip": "10001"
 },
 "cuisine": "American",
 "latitude": 40.749587,
 "longitude": -74.004674,
 "inspection_date": "2026-10-27",
 "inspection_type": "Cycle Inspection / Initial Inspection",
 "action": "Violations were cited in the following area(s).",
 "score": 12,
 "grade": "A",
 "grade_meaning": "Grade A (score 0-13)",
 "grade_date": "2026-10-27",
 "violation_count": 3,
 "source_dataset": {
  "name": "DOHMH New York City Restaurant Inspection Results",
  "id": "43nn-pn8j",
  "portal": "https://data.cityofnewyork.us"
 },
 "source_city": "New York City",
 "modifications": "Fields selected and renamed; phone omitted.",
 "record_date": "2026-10-29",
 "retrieved_at": "2026-10-30T10:27:34Z"
}
```

**Data source, notices and limits**

- Source: New York City Department of Health and Mental Hygiene, NYC Open Data, dataset 43nn-pn8j (version: record_date). Modified: fields selected and renamed; phone omitted. Source: City of Austin, Austin Public Health, data.austintexas.gov, dataset ecmv-9xxi. Public domain.
- Unofficial. Not affiliated with or endorsed by any city.
- Provided as is; may be incomplete or out of date.
- Includes violations that may not yet be adjudicated (NYC). Results reflect the inspector's findings at the time of inspection. NYC scores: lower is better. Austin scores: out of 100, higher is better.
- Do not use this data to re-identify or contact individuals.
- Food establishment inspections as each city publishes them; no extra filtering by establishment type. Business name, address and inspection results; no phone numbers, inspector names or comments.
- New York City cached up to 12 hours (updated daily at source), Austin up to 24 hours (updated about every two weeks); retrieved_at shows when.
- Removal requests: support@lepidata.com (handled within 30 days).
- Unofficial data provided as is. Not an official health rating.
- Records listed for removal on request are filtered out of every run.
- Source capacity is shared by all users of this tool. When it runs low or the source rate limits, runs stop cleanly with a free `service-busy` row. Hard cap: 300 source calls per run, paced (about one call every 1 s).
- Free-plan accounts get up to 100 results per run.

**Pricing.** Pay per result row, flat on every plan, no start fee. Status rows are free.

Contact: support@lepidata.com

# Actor input Schema

## `endpoint` (type: `string`):

Which data to fetch.

## `ids` (type: `array`):

For modes that need an ID: nyc-facility: NYC CAMIS numbers; austin-facility: Austin facility IDs. Up to 200 per run.

## `filters` (type: `object`):

Query filters for the chosen mode, as JSON (see the README for each mode's filters). Unknown filters return a free invalid-input row.

## `maxItems` (type: `integer`):

Stop after this many paid result rows.

## Actor input object example

```json
{
  "endpoint": "inspections",
  "filters": {
    "city": "nyc",
    "zip": "10001"
  },
  "maxItems": 20
}
```

# Actor output Schema

## `items` (type: `string`):

All rows from this run.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "endpoint": "inspections",
    "filters": {
        "city": "nyc",
        "zip": "10001"
    },
    "maxItems": 20
};

// Run the Actor and wait for it to finish
const run = await client.actor("lepidata/restaurant-health-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "endpoint": "inspections",
    "filters": {
        "city": "nyc",
        "zip": "10001",
    },
    "maxItems": 20,
}

# Run the Actor and wait for it to finish
run = client.actor("lepidata/restaurant-health-inspections").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "endpoint": "inspections",
  "filters": {
    "city": "nyc",
    "zip": "10001"
  },
  "maxItems": 20
}' |
apify call lepidata/restaurant-health-inspections --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,lepidata/restaurant-health-inspections"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/0q5JiCdbO0zzXXk4i/builds/Py70cb5vkhHfjqBWL/openapi.json