# Food Safety Inspection Scanner — Restaurant Health Inspections (`mibedk/food-inspection-scanner`) Actor Extract restaurant food-safety and health-inspection records from public government open-data portals. Ships with NYC DOHMH (updated daily); works with any Socrata open-data city by configuration. Normalized JSON, filters for borough, cuisine, grade, critical violations, and recency. - **URL**: https://apify.com/mibedk/food-inspection-scanner.md - **Developed by:** [Mikkel Bech-Hansen](https://apify.com/mibedk) (community) - **Categories:** Business, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.36 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Food Safety Inspection Scanner — Restaurant Health Inspections (NYC + Socrata Cities) Extract restaurant food-safety and health-inspection records from public government open-data portals as clean, normalized JSON — ready for spreadsheets, dashboards, databases, and automation pipelines. Ships preconfigured for the **NYC Department of Health (DOHMH) Restaurant Inspection Results** dataset, which is updated **daily** and contains every inspection and violation cited at New York City food-service establishments. The backend is generic Socrata — point it at **any Socrata open-data portal** (Chicago, Los Angeles, Boston, Austin, and 200+ other cities) by changing two input fields. ### What it does - Queries the city's official open-data API (no scraping, no CAPTCHAs, no broken HTML parsers — the data comes straight from the government source) - Filters by **borough**, **cuisine**, **ZIP code**, **inspection grade** (A/B/C), **critical violations only**, and **recency** (last N days) - Normalizes every row into a stable record schema with deterministic IDs (safe for incremental syncs and deduplication) - Handles throttling and transient errors with retry + exponential backoff - Runs in seconds — a single API request per run, minimal compute cost ### Input | Field | Type | Default | Description | |---|---|---|---| | `days_back` | integer | `30` | Only include inspections from the last N days | | `max_items` | integer | `100` | Maximum records to return (cap: 5,000) | | `boro` | select | `Any` | NYC borough filter | | `cuisine` | string | — | Cuisine keyword, e.g. `Pizza`, `Chinese`, `Bakery` | | `zipcode` | string | — | Exact ZIP code filter | | `grade` | select | `Any` | Letter grade filter (A / B / C) | | `critical_only` | boolean | `false` | Only critical violations | | `socrata_domain` | string | `data.cityofnewyork.us` | Any Socrata open-data portal | | `dataset_id` | string | `43nn-pn8j` | Socrata dataset ID | ### Output One JSON record per inspection violation, e.g.: ```json { "id": "NTAxODA0NTJ8MjAyNi0wNy", "source": "socrata:data.cityofnewyork.us/43nn-pn8j", "restaurant_id": "50180452", "restaurant_name": "ALL LIN COMING INC", "boro": "Staten Island", "address": "1022 ROSSVILLE AVENUE", "zipcode": "10309", "phone": "3472799411", "cuisine_description": "Chinese", "inspection_date": "2026-07-18", "inspection_type": "Cycle Inspection / Initial Inspection", "action": "Violations were cited in the following area(s).", "violation_code": "04L", "violation_description": "Evidence of mice or live mice present in facility...", "critical_flag": "Critical", "score": 17, "grade": "B", "grade_date": "2026-07-18", "record_date": "2026-07-21", "latitude": 40.552129043699, "longitude": -74.212092661776, "url": "https://data.cityofnewyork.us/resource/43nn-pn8j.json?camis=50180452" } ```` Export as JSON, CSV, or Excel from the Apify dataset, or consume via API. ### Real-world examples #### Example 1 — Weekly Manhattan pizza inspection roundup A food-delivery platform wants to display health grades next to restaurant listings. ```json { "boro": "Manhattan", "cuisine": "Pizza", "days_back": 7, "max_items": 500 } ``` #### Example 2 — Critical violations only, last 14 days A compliance auditor needs to flag restaurants with serious hygiene issues for follow-up. ```json { "critical_only": true, "days_back": 14, "max_items": 1000 } ``` #### Example 3 — Chicago food inspections A market-research firm tracks inspection trends across multiple cities. ```json { "socrata_domain": "data.cityofchicago.org", "dataset_id": "4ijn-s7e5", "days_back": 30, "max_items": 2000 } ``` ### Use cases - **Delivery platforms** — enrich restaurant listings with official health grades and violation histories - **Restaurant chains & franchise auditors** — monitor your own locations for new violations and grade changes (schedule daily runs + webhooks) - **Insurance underwriting** — food-safety risk signals for hospitality clients - **Commercial real estate** — due-diligence on food-service tenants - **Journalism & research** — track enforcement patterns by borough, cuisine, or ZIP code - **Competitive intelligence** — monitor compliance trends across a market ### Other cities Any city on a Socrata open-data portal works. Examples: | City | `socrata_domain` | Example dataset | |---|---|---| | NYC (default) | `data.cityofnewyork.us` | `43nn-pn8j` (restaurant inspections) | | Chicago | `data.cityofchicago.org` | `4ijn-s7e5` (food inspections) | | Austin | `data.austintexas.gov` | `ecmv-9xxi` (food establishment inspections) | Note: filter fields (`boro`, `grade`, `cuisine`, `zipcode`) are NYC-specific column names; for other datasets use `days_back` / `max_items` and check the dataset's column names. Date, limit, and ordering filters work everywhere. ### Scheduling & integrations Run on a schedule (daily/weekly) directly in Apify, or trigger via API, Zapier, Make, or webhooks. Use deterministic record IDs for incremental syncs into your own database. ### Cost & reliability - **Flat pricing:** $0.00036 per result + $0.001 run start fee — 1,000 records costs $0.36 - **Deterministic:** no headless browser, no LLM, no paid third-party APIs - **Cheap:** one HTTP request per run, typically <10 seconds of compute - **Data license:** public government open data, free to reuse - **Source stability:** Socrata (Tyler Technologies) powers 200+ government portals; schemas are stable and changes are announced # Actor input Schema ## `socrata_domain` (type: `string`): Open-data portal domain (default: data.cityofnewyork.us for NYC). Change to any Socrata city portal, e.g. data.cityofchicago.org. ## `dataset_id` (type: `string`): Socrata dataset identifier (default: 43nn-pn8j = NYC DOHMH Restaurant Inspection Results). ## `boro` (type: `string`): Filter by NYC borough (Any = no filter). Ignored for non-NYC datasets. ## `cuisine` (type: `string`): Case-insensitive keyword matched against the cuisine description, e.g. 'Pizza', 'Chinese', 'Bakery'. Empty = no filter. ## `zipcode` (type: `string`): Filter by exact ZIP code. Empty = no filter. ## `grade` (type: `string`): Filter by letter grade (Any = no filter). Only inspections that received a grade match. ## `critical_only` (type: `boolean`): If true, return only violation rows flagged as Critical. ## `days_back` (type: `integer`): Only include inspections from the last N days (default: 30). ## `max_items` (type: `integer`): Maximum number of records to return (default: 100, hard cap: 5000). ## Actor input object example ```json { "socrata_domain": "data.cityofnewyork.us", "dataset_id": "43nn-pn8j", "boro": "Any", "grade": "Any", "critical_only": false, "days_back": 30, "max_items": 100 } ``` # 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 = { "socrata_domain": "data.cityofnewyork.us", "dataset_id": "43nn-pn8j" }; // Run the Actor and wait for it to finish const run = await client.actor("mibedk/food-inspection-scanner").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 = { "socrata_domain": "data.cityofnewyork.us", "dataset_id": "43nn-pn8j", } # Run the Actor and wait for it to finish run = client.actor("mibedk/food-inspection-scanner").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 '{ "socrata_domain": "data.cityofnewyork.us", "dataset_id": "43nn-pn8j" }' | apify call mibedk/food-inspection-scanner --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=mibedk/food-inspection-scanner", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Food Safety Inspection Scanner — Restaurant Health Inspections", "description": "Extract restaurant food-safety and health-inspection records from public government open-data portals. Ships with NYC DOHMH (updated daily); works with any Socrata open-data city by configuration. Normalized JSON, filters for borough, cuisine, grade, critical violations, and recency.", "version": "0.3", "x-build-id": "dq5nqrSQgea3RmEm8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/mibedk~food-inspection-scanner/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-mibedk-food-inspection-scanner", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/mibedk~food-inspection-scanner/runs": { "post": { "operationId": "runs-sync-mibedk-food-inspection-scanner", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/mibedk~food-inspection-scanner/run-sync": { "post": { "operationId": "run-sync-mibedk-food-inspection-scanner", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "socrata_domain": { "title": "Socrata domain", "type": "string", "description": "Open-data portal domain (default: data.cityofnewyork.us for NYC). Change to any Socrata city portal, e.g. data.cityofchicago.org.", "default": "data.cityofnewyork.us" }, "dataset_id": { "title": "Socrata dataset ID", "type": "string", "description": "Socrata dataset identifier (default: 43nn-pn8j = NYC DOHMH Restaurant Inspection Results).", "default": "43nn-pn8j" }, "boro": { "title": "Borough", "enum": [ "Any", "Manhattan", "Brooklyn", "Queens", "Bronx", "Staten Island" ], "type": "string", "description": "Filter by NYC borough (Any = no filter). Ignored for non-NYC datasets.", "default": "Any" }, "cuisine": { "title": "Cuisine keyword", "type": "string", "description": "Case-insensitive keyword matched against the cuisine description, e.g. 'Pizza', 'Chinese', 'Bakery'. Empty = no filter." }, "zipcode": { "title": "ZIP code", "type": "string", "description": "Filter by exact ZIP code. Empty = no filter." }, "grade": { "title": "Inspection grade", "enum": [ "Any", "A", "B", "C" ], "type": "string", "description": "Filter by letter grade (Any = no filter). Only inspections that received a grade match.", "default": "Any" }, "critical_only": { "title": "Critical violations only", "type": "boolean", "description": "If true, return only violation rows flagged as Critical.", "default": false }, "days_back": { "title": "Days back", "minimum": 1, "maximum": 3650, "type": "integer", "description": "Only include inspections from the last N days (default: 30).", "default": 30 }, "max_items": { "title": "Maximum items", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum number of records to return (default: 100, hard cap: 5000).", "default": 100 } } }, "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 } } } } } } } } } } ```