# Seattle Restaurant Inspections Scraper (`muhammadafzal/seattle-restaurant-inspections-scraper`) Actor

Extract public Seattle restaurant and food establishment inspection records from King County Open Data, including inspection dates, results, scores, risk categories, grades, closure indicators, and grouped violations.

- **URL**: https://apify.com/muhammadafzal/seattle-restaurant-inspections-scraper.md
- **Developed by:** [Muhammad Afzal](https://apify.com/muhammadafzal) (community)
- **Categories:** Other, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 seattle inspection records

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

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

## 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

## Seattle Restaurant Inspections Scraper

Extract public food establishment inspection records for Seattle from the [King County Open Data Food Establishment Inspection Data](https://data.kingcounty.gov/d/r878-4sxa) dataset.

Each output item is one unique inspection, even though the source may contain multiple rows for that inspection when multiple violations were recorded. Records include the establishment name, address, inspection date/type/result, score, risk category, grade, closure indicator, and optionally grouped violations.

### Input

```json
{
  "restaurantName": "Canlis",
  "city": "SEATTLE",
  "inspectionDateFrom": "2024-01-01",
  "inspectionDateTo": "2025-12-31",
  "maxResults": 50,
  "includeViolations": true
}
```

`city` defaults to `SEATTLE`; change it to another King County city when needed. `maxResults` is bounded to 500. Empty matches return a truthful `EMPTY` summary and no dataset records.

### Data source and limitations

The actor uses the public Socrata endpoint owned by Public Health – Seattle & King County. Inspection reports are snapshots of conditions observed at the time of inspection, not a guarantee of a restaurant's current condition. The dataset can contain non-restaurant food establishments and may include duplicate violation rows for one inspection; this actor aggregates those rows.

### Pricing

Pay per event: one run-start event plus one dataset-item event for each unique inspection record written. Empty and failed runs do not write result records.

### Use cases

- Schedule repeatable collection and export results to downstream workflows.
- Run a one-off research job and export the structured result as JSON, CSV, Excel, XML, or RSS from Apify.
- Schedule the same input to monitor changes over time and send completed datasets to a webhook or integration.
- Feed schema-shaped records into a database, spreadsheet, BI tool, or AI workflow with the source URL retained for verification.

### Output example

```json
{}
```

The exact fields depend on the selected input and what the public source exposes. Use the dataset schema as the machine-readable contract and retain source URLs for verification.

### Run Seattle Restaurant Inspections Scraper with the Apify API

```javascript
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('muhammadafzal/seattle-restaurant-inspections-scraper').call({
  "restaurantName": "",
  "city": "SEATTLE",
  "maxResults": 50,
  "includeViolations": true
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
```

You can also run the Actor from Apify Console, schedules, webhooks, the REST API, Make, Zapier, n8n, or the hosted Apify MCP server.

### Responsible use

Use this Actor only for data you are authorized to access. Follow the target website's terms, robots and access policies, and applicable privacy, database, copyright, anti-spam, and data-protection laws. Do not use it to bypass authentication or other access controls, collect private data, harass people, or make high-impact decisions without independent verification.

### Support

When reporting a problem, include the **Actor run ID**, a redacted input, the expected result, and a small public example URL when applicable. Do not post API tokens, cookies, credentials, or personal data in an issue.

### Frequently asked questions

#### Can I schedule Seattle Restaurant Inspections Scraper?

Yes. Use an Apify schedule to run the same saved input at a chosen interval, then connect a webhook or integration to process the dataset when the run finishes.

#### How should I test a new input?

Begin with the prefilled example or a small limit. Confirm that the output fields, source coverage, runtime, and live charges match your workflow before increasing the scope.

#### How do I export the results?

Open the run's default dataset in Apify Console and export JSON, CSV, Excel, XML, or RSS. Applications can retrieve the same records through the Apify API client or REST dataset endpoint.

#### Can an AI agent call this Actor?

Yes. Add `muhammadafzal/seattle-restaurant-inspections-scraper` through the hosted Apify MCP server or call it through the API. The Actor's input and dataset schemas help agents construct valid requests and interpret returned records.

### Recommended workflow

1. **Define the smallest useful scope.** Choose a representative public URL, query, identifier, or filter and keep the first result limit low.
2. **Run and inspect.** Check the run log, dataset item count, field coverage, source URLs, and live event or usage charges.
3. **Validate downstream assumptions.** Confirm nullable fields, deduplication keys, timestamps, and any locale-specific formats before importing records into another system.
4. **Scale gradually.** Increase limits or scheduling frequency only after the small run behaves as expected. Use Apify's maximum-cost and timeout controls to bound large jobs.
5. **Monitor changes.** Keep a small known-good input as a canary. If the source layout or API changes, compare the new dataset with a previously validated run and report the run ID when requesting support.

For recurring workflows, store the exact Actor input with your pipeline configuration. This makes runs reproducible and helps distinguish a source-data change from an input change.

# Actor input Schema

## `restaurantName` (type: `string`):

Optional restaurant or food establishment name to search; use a partial name such as "Canlis". Leave empty for all Seattle establishments.

## `city` (type: `string`):

City filter for the public King County dataset; use a city such as "SEATTLE". Defaults to Seattle and does not include the whole county unless changed.

## `inspectionDateFrom` (type: `string`):

Optional inclusive start date in YYYY-MM-DD format, for example 2025-01-01.

## `inspectionDateTo` (type: `string`):

Optional inclusive end date in YYYY-MM-DD format, for example 2025-12-31.

## `maxResults` (type: `integer`):

Maximum number of unique inspection records to return; bounded from 1 to 500.

## `includeViolations` (type: `boolean`):

When enabled, include violation descriptions and points grouped under each inspection.

## Actor input object example

```json
{
  "restaurantName": "",
  "city": "SEATTLE",
  "maxResults": 50,
  "includeViolations": true
}
```

# Actor output Schema

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

No description

## `summary` (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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("muhammadafzal/seattle-restaurant-inspections-scraper").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("muhammadafzal/seattle-restaurant-inspections-scraper").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 '{}' |
apify call muhammadafzal/seattle-restaurant-inspections-scraper --silent --output-dataset

```

## MCP server setup

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

```

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/OCvvJvEIePkAuPSur/builds/qghlkcwUkfliKFk2i/openapi.json