Seattle Restaurant Inspections Scraper

Extract public food establishment inspection records for Seattle from the King County Open Data Food Establishment Inspection Data 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

{
  "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

{}

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

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.