# Restaurant Fit & Dietary Intelligence API (`plenteous_humidifier/restaurant-fit-intelligence`) Actor

Find public restaurants near a location and rank them against dietary, accessibility, seating, and atmosphere constraints with source-backed evidence.

- **URL**: https://apify.com/plenteous\_humidifier/restaurant-fit-intelligence.md
- **Developed by:** [Luigy Gabriel](https://apify.com/plenteous_humidifier) (community)
- **Categories:** Automation, Business, Travel
- **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $20.00 / 1,000 venue analyzeds

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 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

## Restaurant Fit & Dietary Intelligence API

Find public restaurants, cafes, and bars near a location and rank them against practical constraints such as vegan options, outdoor seating, wheelchair access, private rooms, live music, or a family-friendly setup.

The Actor uses public OpenStreetMap records and, when enabled, public venue websites. Every match includes its source. An absent attribute is returned as unknown/missing rather than invented.

### Input

- `location`: city, neighborhood, address, or landmark.
- `radiusMeters`: 100–25,000 meters.
- `constraints`: up to 20 requested attributes.
- `maxResults`: hard spend and runtime cap, maximum 50.
- `includeWebsiteEvidence`: checks public venue websites without logging in.
- `restaurants`: optional API-supplied records for deterministic enrichment.

### Output

Each dataset row contains the venue, coordinates, public website, fit score, confidence, matched and missing constraints, and source-backed evidence. `OUTPUT` contains the complete sorted summary.

### Responsible use

This is research data, not a reservation or safety guarantee. Menus, accessibility, hours, and policies change. Confirm important requirements directly with the venue. The Actor never books, logs in, or accesses private customer data.

### Pricing design

Pay per successfully analyzed venue. Failed discovery or blocked websites do not create billable venue rows.

# Actor input Schema

## `location` (type: `string`):

City, neighborhood, address, or landmark.

## `radiusMeters` (type: `integer`):

Maximum OpenStreetMap discovery radius around the resolved location.

## `constraints` (type: `array`):

Examples: vegan, outdoor, private room, family friendly, live music.

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

Hard cap on venue analyses returned by one run.

## `includeWebsiteEvidence` (type: `boolean`):

Read a bounded amount of public venue website text for additional evidence.

## `restaurants` (type: `array`):

Advanced API input. When present, location discovery is skipped.

## Actor input object example

```json
{
  "location": "Manhattan, New York",
  "radiusMeters": 3000,
  "constraints": [
    "vegetarian",
    "outdoor"
  ],
  "maxResults": 20,
  "includeWebsiteEvidence": 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("plenteous_humidifier/restaurant-fit-intelligence").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("plenteous_humidifier/restaurant-fit-intelligence").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 '{}' |
apify call plenteous_humidifier/restaurant-fit-intelligence --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/peoaspj9g2QmlQzh0/builds/HTcqGJMmTXwrfXCHk/openapi.json