# Chicago Restaurant Inspections (`deepztack/chicago-restaurant-inspections`) Actor

Get fresh Chicago restaurant health inspection results (Pass/Fail) from the official city open-data API, with business name, facility type, risk level, address, and date.

- **URL**: https://apify.com/deepztack/chicago-restaurant-inspections.md
- **Developed by:** [deepztack](https://apify.com/deepztack) (community)
- **Categories:** Business, Marketing
- **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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Chicago Restaurant Inspections

Get fresh Chicago restaurant health inspection results from the official City of Chicago open-data API, including business name, facility type, risk level, inspection date, address, and Pass/Fail result.

### What this Actor does

This Actor queries Chicago's official Socrata dataset for food inspections and returns clean records with:

- inspection ID and business name,
- facility type and risk level,
- inspection date, type, and result (Pass/Fail/Conditions),
- address and geolocation,
- source link,
- simplified result category.

Official source:

- `https://data.cityofchicago.org/resource/4ijn-s7e5.json`

### Who it is for

- restaurant owners and managers tracking their compliance record,
- food industry researchers analyzing health trends,
- health compliance consultants,
- commercial brokers assessing areas by restaurant quality.

### Input

```json
{
  "startDate": "2026-06-01",
  "endDate": "2026-07-04",
  "results": ["Fail", "Pass w/ Conditions"],
  "facilityTypes": ["Restaurant"],
  "maxItems": 100
}
```

All filters are optional except `maxItems`.

### Output example

```json
{
  "inspectionId": "2639280",
  "businessName": "TEST RESTAURANT INC",
  "facilityType": "Restaurant",
  "risk": "Risk 1 (High)",
  "address": "100 N STATE ST",
  "city": "CHICAGO",
  "inspectionDate": "2026-07-01",
  "inspectionType": "Canvass",
  "result": "Pass",
  "resultType": "pass",
  "latitude": 41.8827,
  "longitude": -87.6278,
  "sourceUrl": "https://data.cityofchicago.org/Food-Health/Food-Inspections/4ijn-s7e5/2639280",
  "scrapedAt": "2026-07-04T12:00:00+00:00"
}
```

### Result types

- `pass`: Pass
- `fail`: Fail
- `conditional`: Pass w/ Conditions
- `other`: Business Not Located, No Entry, Not Ready, Out of Business
- `unknown`: missing result

### Responsible use

This Actor uses public food inspection data from the City of Chicago. Use the output responsibly and in accordance with applicable laws.

### Limitations

- Not affiliated with or endorsed by the City of Chicago.
- Dataset fields depend on the official source and may be blank for some records.
- Result categories are simplified from the source; always verify results against the source URL.

### Development

```bash
python3 -m pytest tests/test_chicago_inspections.py -q

npx apify-cli run
```

### Changelog

- v0.1.0 — Initial Chicago restaurant inspection extraction from official Socrata API.

# Actor input Schema

## `startDate` (type: `string`):

Only include inspections on or after this date (YYYY-MM-DD).

## `endDate` (type: `string`):

Only include inspections on or before this date (YYYY-MM-DD).

## `results` (type: `array`):

Filter by inspection result. Leave empty for all results.

## `facilityTypes` (type: `array`):

Filter by facility type, for example Restaurant, School, Grocery Store.

## `riskLevels` (type: `array`):

Filter by risk level: Risk 1 (High), Risk 2 (Medium), Risk 3 (Low).

## `minLatitude` (type: `number`):

Southern boundary for geographic filter.

## `maxLatitude` (type: `number`):

Northern boundary for geographic filter.

## `minLongitude` (type: `number`):

Western boundary for geographic filter.

## `maxLongitude` (type: `number`):

Eastern boundary for geographic filter.

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

Maximum number of inspection records to return. Capped at 10000.

## Actor input object example

```json
{
  "results": [
    "Fail",
    "Pass w/ Conditions"
  ],
  "maxItems": 100
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# 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 = {
    "results": [
        "Fail",
        "Pass w/ Conditions"
    ],
    "maxItems": 100
};

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

# Prepare the Actor input
run_input = {
    "results": [
        "Fail",
        "Pass w/ Conditions",
    ],
    "maxItems": 100,
}

# Run the Actor and wait for it to finish
run = client.actor("deepztack/chicago-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 '{
  "results": [
    "Fail",
    "Pass w/ Conditions"
  ],
  "maxItems": 100
}' |
apify call deepztack/chicago-restaurant-inspections --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=deepztack/chicago-restaurant-inspections",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/2Pa6ZP23jo4UTLP7F/builds/LBN6B6JJ601khdEom/openapi.json