# Weather Forecast API (`agentx/weather-forecast-api`) Actor

Weather Forecast API is a weather forecast api that resolves one place into 54-field daily forecast or historical rows with hourly weather and best-effort air quality. Request 1–16 forecast days, or a historical range of any length — thirty years in one run.

- **URL**: https://apify.com/agentx/weather-forecast-api.md
- **Developed by:** [AgentX](https://apify.com/agentx) (community)
- **Categories:** Other, Developer tools, Automation
- **Stats:** 4 total users, 1 monthly users, 100.0% runs succeeded, 3 bookmarks
- **User rating**: 4.00 out of 5 stars

## Pricing

from $1.50 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

**Weather Forecast API is a weather forecast api that resolves one place into daily forecast or historical weather rows with hourly detail and best-effort air quality.** Each Dataset item follows a documented 54-field contract and keeps the resolved coordinates, timezone, units, source values, and processing provenance together.

[![Apify Users](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.apify.com%2Fv2%2Facts%2Fagentx~weather-forecast-api\&query=%24.data.stats.totalUsers\&label=Apify%20Users\&color=ff9013\&style=for-the-badge)](https://apify.com/agentx/weather-forecast-api) [![Apify Runs](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.apify.com%2Fv2%2Facts%2Fagentx~weather-forecast-api\&query=%24.data.stats.totalRuns\&label=Apify%20Runs\&color=ff9013\&style=for-the-badge)](https://apify.com/agentx/weather-forecast-api) [![$0.002 / result](https://img.shields.io/badge/%240.002%20%2F%20result-4285f4?style=for-the-badge)](https://apify.com/agentx/weather-forecast-api/pricing) [![54 weather fields](https://img.shields.io/badge/54%20weather%20fields-34a853?style=for-the-badge)](https://console.apify.com/actors/Yn1ksjorQhCtBcLN6/input) [![API + MCP ready](https://img.shields.io/badge/API%20%2B%20MCP%20ready-fbbc05?style=for-the-badge)](https://apify.com/agentx/weather-forecast-api/api)

- **Forecast and history in one contract** — select 1–16 forecast days or a paired historical date range without changing the output shape.
- **Daily and hourly weather together** — use daily temperature, wind, precipitation, sunlight, and nested hourly values in the same row.
- **Resolved place context** — retain the matched location, country, region, city, coordinates, and timezone for reproducible downstream joins.
- **Explicit air-quality coverage** — receive current and hourly air-quality objects when available plus a plain-language note when coverage differs.

The smallest useful run is one forecast day: one $0.01 Actor Start plus one $0.002 Result costs $0.012 when one row is written.

### Why Choose Weather Forecast API

**One place becomes analysis-ready records.** Submit a country, region, city, town, or district query and receive one resolved match with coordinates and daily rows, avoiding a separate geocoding join in ordinary workflows.

**Forecast and historical modes share 54 fields.** A scheduled forecast and a bounded historical pull can feed the same table, database model, or automation because identifiers, units, weather measures, nested objects, and provenance remain consistently named.

**Daily decisions retain hourly evidence.** Top-level values summarize each day, while `hourly_forecast` preserves the time-grained temperatures, weather labels, wind, precipitation, and related source values needed to explain a daily alert.

**Missing values stay distinguishable.** The Actor does not turn unavailable source values into invented zeroes. Air-quality gaps carry `air_quality_note`, and current-condition fields appear only where the source observation belongs.

**Row counts are known before the run works.** Forecast mode accepts 1–16 days. A historical range has no length limit, and because both endpoints are inclusive the exact row count follows from the dates — the run states it in its first status line, before any charge.

Weather variables follow the official [Open-Meteo forecast documentation](https://open-meteo.com/en/docs), while historical mode uses its documented [Historical Weather API](https://open-meteo.com/en/docs/historical-weather-api). These sources describe modeled weather data; they are not official local warnings or station certifications.

### Quick Start Guide

#### 1. Configure the location and window

Open the Input tab. A working `Tokyo, Japan` example is prefilled with three forecast days. Keep that input, change `days` from 1 to 16, or provide both `start_date` and `end_date` to request history.

#### 2. Run the Actor

Select **Start**. The Actor validates the window, resolves one place, retrieves weather and best-effort air quality, and writes one Dataset item for each returned day.

#### 3. Collect the daily rows

Open the default Dataset to preview results or export JSON, CSV, Excel, XML, or RSS. In an automated flow, preserve `defaultDatasetId` from the run object and read items from that Dataset after the run reaches a terminal state.

### Input Parameters

Weather Forecast API requires one location; forecast length and a paired historical range are optional controls, and dates take precedence over `days` when both dates are supplied.

| Parameter | Type | Required | Description | Example |
| --- | --- | --- | --- | --- |
| `location` | string | Yes | Non-empty country, region, city, town, or district query resolved to one place. Add regional context when a name is ambiguous. | `Tokyo, Japan` |
| `days` | integer | No | Forecast length from 1 to 16. Runtime uses 3 when omitted and ignores it in historical mode. | `1` |
| `start_date` | string | No | Range start in `YYYY-MM-DD` or a past-relative form such as `30 days`; requires `end_date`. | `2026-07-01` |
| `end_date` | string | No | Range end using the same syntax; it cannot precede the start. No maximum length. | `2026-07-07` |

The shared Tokyo forecast scenario used throughout these examples is:

```json
{
  "location": "Tokyo, Japan",
  "days": 1
}
```

Historical relative values accept singular or plural day, week, month, and year units. A relative month means 30 days and a relative year means 365 days. Both resolved dates are inclusive: equal endpoints request one day, endpoints 366 days apart return 367 rows, and a thirty-year range returns 10,958.

| Window | Mode | Maximum returned rows |
| --- | --- | ---: |
| `days: 1` | forecast | 1 |
| `days: 16` | forecast | 16 |
| Same start and end date | historical | 1 |
| End 366 days after start | historical | 367 |
| `start_date: 30 years` | historical | 10,958 |

### Output Data Schema

Each Dataset row represents one returned calendar day and contains 54 top-level fields; nested hourly arrays are abbreviated below, while a real run keeps every source row returned for that day.

| Group | Fields | What they provide |
| --- | --- | --- |
| Request and place | `status`, `mode`, `start_date`, `end_date`, `location`, `country`, `region`, `city`, `latitude`, `longitude`, `timezone` | Resolution and window context for joins and diagnostics |
| Units and daily identity | `temperature_unit`, `wind_speed_unit`, `date`, `weather` | Units and the represented calendar day |
| Temperature and wind | `temperature_max`, `temperature_min`, `temperature_mean`, `apparent_temperature_max`, `apparent_temperature_min`, `wind_speed_max`, `wind_gusts_max`, `wind_direction_dominant`, `wind_direction_label` | Daily ranges and dominant wind conditions |
| Water and sunlight | `precipitation_sum`, `precipitation_probability_max`, `rain_sum`, `snowfall_sum`, `precipitation_hours`, `uv_index_max`, `sunrise`, `sunset`, `daylight_hours`, `sunshine_hours` | Daily precipitation and solar measures |
| Current conditions | `current_temperature`, `current_apparent_temperature`, `current_humidity`, `current_weather`, `current_cloud_cover`, `current_pressure`, `current_visibility_km`, `current_uv_index`, `current_wind_speed`, `current_wind_direction`, `current_wind_direction_label`, `current_wind_gusts`, `current_is_day`, `current_precipitation` | Present observation fields on the matching forecast date |
| Nested and provenance | `air_quality_note`, `hourly_forecast`, `air_quality_current`, `air_quality_hourly`, `processor`, `processed_at` | Time-grained objects, coverage explanation, and processing stamps |

Abbreviated example — the arrays are truncated here; a real one-day run returns all hourly values supplied for that day and still exposes the full 54-field top-level contract:

```json
{
  "status": "success",
  "mode": "forecast",
  "location": "Tokyo, Japan",
  "country": "Japan",
  "latitude": 35.6762,
  "longitude": 139.6503,
  "timezone": "Asia/Tokyo",
  "temperature_unit": "celsius",
  "wind_speed_unit": "kmh",
  "date": "2026-08-13",
  "weather": "Mainly clear",
  "temperature_max": 33.1,
  "temperature_min": 26.4,
  "precipitation_probability_max": 20,
  "hourly_forecast": [
    {"time": "2026-08-13T12:00", "temperature": 31.8}
  ],
  "air_quality_current": {"european_aqi": 24},
  "air_quality_hourly": [
    {"time": "2026-08-13T12:00", "european_aqi": 24}
  ],
  "processor": "https://apify.com/agentx/weather-forecast-api",
  "processed_at": "2026-08-13T19:00:00+00:00"
}
```

Unavailable source values remain null or empty instead of becoming artificial measurements. Export the same Dataset as JSON for applications, CSV or Excel for analysis, or XML and RSS for compatible workflows.

### Integration Examples

Weather Forecast API uses Actor ID `Yn1ksjorQhCtBcLN6` with the same Tokyo one-day JSON input across REST, official clients, automation platforms, and MCP.

#### Actor ID

```text
Yn1ksjorQhCtBcLN6
```

The name form `agentx/weather-forecast-api` is also supported where an Apify interface accepts Actor names.

#### HTTP

```bash
curl -X POST "https://api.apify.com/v2/acts/Yn1ksjorQhCtBcLN6/runs?waitForFinish=120" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"location":"Tokyo, Japan","days":1}'
```

Read results through the returned `defaultDatasetId` rather than assuming a successful process always wrote rows.

#### Python

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("Yn1ksjorQhCtBcLN6").call(
    run_input={"location": "Tokyo, Japan", "days": 1}
)
items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
```

#### JavaScript

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

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('Yn1ksjorQhCtBcLN6').call({
  location: 'Tokyo, Japan',
  days: 1,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
```

#### Make.com

Add an Apify **Run an Actor** module, enter `Yn1ksjorQhCtBcLN6`, paste `{"location":"Tokyo, Japan","days":1}`, and map `defaultDatasetId` into a **Get Dataset Items** module.

#### n8n

Use an Apify node or authenticated HTTP Request node to start `Yn1ksjorQhCtBcLN6` with the Tokyo JSON, wait for a terminal run status, then request items from the run's `defaultDatasetId`.

#### MCP

Connect the Apify MCP server, select `agentx/weather-forecast-api`, send `location: Tokyo, Japan` and `days: 1`, then ask the client to return the default Dataset items. The public schema lets an MCP client discover the same four input fields before calling the Actor.

### Pricing

Weather Forecast API costs $0.002 per Result on the Free plan and less on paid plans, plus one $0.01 Actor Start per run; the smallest one-day run totals $0.012 when one row is written.

| Billed event | Price | When charged |
| --- | ---: | --- |
| Actor Start | $0.01 | Once when a run starts, including a run that later writes zero rows |
| Result | $0.002 | Once per daily Dataset row written; $0.0018 on Bronze, $0.0016 on Silver, $0.0015 on Gold and above |

The 256 MB runtime memory is included in the Actor's configured run shape; pricing follows the two billed events above. A one-day forecast is $0.01 + 1 × $0.002 = **$0.012**. A typical seven-day forecast that writes seven rows is $0.01 + 7 × $0.002 = **$0.024**. A full year of history writes 366 rows for $0.01 + 366 × $0.002 = **$0.742**. One seven-day run is cheaper than seven one-day runs because it pays Actor Start once rather than seven times.

Rows actually written determine Result charges. Failed place resolution or upstream retrieval can still incur Actor Start and return no items. Prices can change; verify the current [Pricing page](https://apify.com/agentx/weather-forecast-api/pricing) before budgeting a production schedule.

### Use Cases

**Operations forecast alerts.** Schedule one place per run and evaluate `precipitation_probability_max`, `snowfall_sum`, `wind_gusts_max`, or `current_visibility_km` before dispatching field teams.

**Event and venue planning.** Combine `date`, `weather`, `temperature_max`, `rain_sum`, `sunrise`, and `sunset` for outdoor staffing, equipment, and timing decisions.

**Historical demand analysis.** Request a bounded date range and join `temperature_mean`, `precipitation_sum`, `sunshine_hours`, latitude, longitude, and timezone to sales, energy, or attendance records.

**Air-quality context.** Store `air_quality_current`, `air_quality_hourly`, and `air_quality_note` beside weather records while treating unavailable coverage explicitly rather than filling it with zero.

**Application data feeds.** Normalize the daily scalar fields into a database and retain `hourly_forecast` as JSON for drill-down views, rules, or model features.

**Multi-location monitoring.** Create one run per unambiguous place, keep the resolved coordinates, and merge results downstream using your own location identifier and run schedule.

### Alternatives

Manual lookup is enough for an occasional human check when no structured history, automation, cost cap, or export is needed. It stops scaling when a workflow must preserve coordinates, hourly arrays, repeated snapshots, and consistent columns.

Open-Meteo's own APIs are the closest source-level alternative. They are preferable when you want to own geocoding, request composition, response joins, scheduling, storage, retries, and schema normalization directly; its [Air Quality API documentation](https://open-meteo.com/en/docs/air-quality-api) also explains the source variables used here.

General data-integration platforms may be preferable when weather is only one connector inside a larger managed stack. Business-intelligence suites may be preferable when your main need is dashboarding data you already possess rather than collecting it.

Choose something else if you need official emergency warnings, aviation-grade or station-certified observations, multiple locations in one Actor input, a guaranteed upstream service level, or safety-critical professional advice.

### Limits and Troubleshooting

- **No Dataset rows →** the place may not resolve or an upstream request may fail → add country or region context, confirm the returned run logs, and retry with a distinctive location.
- **One date is rejected →** historical mode requires a pair → supply both `start_date` and `end_date`, or remove both and use `days`.
- **Date range is rejected →** the end precedes the start → submit valid absolute or past-relative values in order; the range itself has no maximum length.
- **Current fields are null on future days →** current conditions belong to the observation date → use the daily and hourly forecast fields for later dates.
- **Air quality is empty for part of the window →** source coverage can differ from weather coverage → inspect `air_quality_note` and treat `air_quality_current` or `air_quality_hourly` as optional.
- **Fewer days arrive than requested →** source availability controls returned dates → compare `date`, requested mode, Dataset count, and run logs before retrying.
- **A run costs $0.01 but returns zero rows →** Actor Start is billed before retrieval completes → validate location text and upstream availability; Result is billed only for rows written.
- **A place resolves incorrectly →** one geocoding match is selected → include the city, region, and country and verify `latitude`, `longitude`, and `timezone` before using the data.

For a reproducible defect, open an [Issue](https://apify.com/agentx/weather-forecast-api/issues) with the run ID, UTC timestamp, exact input with secrets removed, resolved location if present, Dataset count, and the smallest relevant log excerpt.

### Trust and Reliability

Runs use Apify's managed Actor infrastructure and expose terminal status, logs, Dataset storage, schedules, webhooks, API access, and charge limits through the platform. Billing is transparent at one start event and one event for each daily row written.

The Dataset contract documents 54 top-level fields, including request mode, resolved place, units, daily summaries, current conditions, nested hourly weather, nested air quality, and processing stamps. Source values remain null or empty when unavailable rather than being silently converted to fabricated measurements.

The Actor is designed for reproducible data workflows, not for official warnings. Monitor scheduled runs, validate critical thresholds against an appropriate authority, and keep a fallback for upstream outages when the workflow is operationally important.

### Legal and Compliance

**Data scope.** Weather and air-quality records describe places and modeled environmental conditions. Follow the providers' current licenses, attribution requirements, and usage policies, including the public [Nominatim usage policy](https://operations.osmfoundation.org/policies/nominatim/).

**Privacy.** The Actor does not require personal data, but your surrounding workflow can combine location data with confidential or personal records. Apply appropriate access controls, retention limits, and purpose restrictions to those downstream datasets.

**Terms and decisions.** Use the Actor under Apify's terms and applicable law. Do not represent modeled output as an official warning, medical conclusion, insurance determination, or other regulated professional advice. This documentation is not legal advice.

### Frequently Asked Questions

#### What does this weather forecast api return?

It returns one 54-field Dataset item per source day, including resolved location data, daily weather, current-condition values where applicable, nested hourly weather, best-effort air quality, and processing provenance.

#### Can this historical weather data api use relative dates?

Yes. Both endpoints accept past-relative day, week, month, or year values, as well as `YYYY-MM-DD`; month means 30 days, year means 365 days, and both dates must be supplied together.

#### Is this an hourly weather forecast api by city?

Yes, for one resolved city or other place per run. Each daily row includes `hourly_forecast`; add country or region context to reduce ambiguous place matches.

#### Is there a free weather API?

Open-Meteo provides an official source API with its own current terms, while this Actor is pay per event at $0.01 per start and $0.002 per daily result. Use the source directly when you prefer to own integration and storage.

#### Can I schedule runs to monitor weather over time?

Yes. Apify schedules can run the Actor repeatedly; store each run's `processed_at`, resolved coordinates, mode, and Dataset items so snapshots remain distinguishable.

#### Why are current-condition fields missing from future forecast rows?

Because those fields represent one current observation. They appear on the matching observation date, while future rows use daily summaries and `hourly_forecast`.

#### How many forecast or historical rows can one run return?

Forecast mode accepts 1–16 days. A historical range has no maximum length: both endpoints are inclusive, so 366 days apart returns 367 daily rows and thirty years returns 10,958.

#### Does the Actor guarantee air-quality values?

No. Air quality is best effort and its temporal coverage can differ from weather coverage; inspect `air_quality_note` and allow the nested air-quality fields to be empty.

### Related AgentX Actors

AgentX publishes 77 Actors organized by category below; the three closest options add place, property, or business context to weather records.

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- **[Video Transcript](https://apify.com/agentx/video-transcript)** — multi-platform transcripts
- **[Video Captions Downloader](https://apify.com/agentx/video-captions-downloader)** — caption files
- **[Video to Social Post](https://apify.com/agentx/video-to-social-post)** — video repurposed into social copy
- **[YouTube Transcript](https://apify.com/agentx/youtube-transcript)** — YouTube transcripts
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- **[X Twitter Transcript](https://apify.com/agentx/x-twitter-transcript)** — X video transcripts
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- **[Zoom Recording Downloader](https://apify.com/agentx/zoom-recording-downloader)** — Zoom recording downloads

#### E-Commerce and Retail

- **[All Shopping Scraper](https://apify.com/agentx/all-shopping-scraper)** — multi-platform product data
- **[AliExpress Product Scraper](https://apify.com/agentx/aliexpress-product-scraper)** — AliExpress products
- **[Amazon Storefront Scraper](https://apify.com/agentx/amazon-storefront-scraper)** — Amazon Brand Store pages
- **[Bol Product Scraper](https://apify.com/agentx/bol-product-scraper)** — Bol.com products
- **[eBay Seller Leads API](https://apify.com/agentx/ebay-seller-leads-api)** — eBay seller records
- **[Hepsiburada Product Scraper](https://apify.com/agentx/hepsiburada-product-scraper)** — Hepsiburada products
- **[Kakaku Product Scraper](https://apify.com/agentx/kakaku-product-scraper)** — Kakaku.com products
- **[Rakuten Product Scraper](https://apify.com/agentx/rakuten-product-scraper)** — Rakuten products

#### Classifieds and Automotive

- **[All Vehicle Scraper](https://apify.com/agentx/all-vehicle-scraper)** — multi-platform vehicle listings
- **[AutoTrader Vehicle Search](https://apify.com/agentx/autotrader-vehicle-search)** — AutoTrader US listings
- **[AutoTrader UK Vehicle Search](https://apify.com/agentx/autotrader-uk-vehicle-search)** — AutoTrader UK listings

#### Real Estate

- **[All Property Scraper](https://apify.com/agentx/all-property-scraper)** — multi-platform property listings
- **[Homes Property Scraper](https://apify.com/agentx/homes-property-scraper)** — Homes.com listings
- **[MagicBricks Property Scraper](https://apify.com/agentx/magicbricks-property-scraper)** — MagicBricks listings
- **[NoBroker Property Scraper](https://apify.com/agentx/nobroker-property-scraper)** — NoBroker listings
- **[Property24 Property Scraper](https://apify.com/agentx/property24-property-scraper)** — Property24 listings
- **[Realtor Property Scraper](https://apify.com/agentx/realtor-property-scraper)** — Realtor.com listings
- **[Zillow Property Scraper](https://apify.com/agentx/zillow-property-scraper)** — Zillow listings

### Support and Community

Ask about forecast windows, historical dates, place resolution, hourly weather, or air-quality coverage in the [AgentX community on Telegram](https://t.me/Apify_Actor); for a reproducible bug, open an [Issue](https://apify.com/agentx/weather-forecast-api/issues) with the run ID and the exact input.

AgentX is an Arcyton brand — [arcyton.com](https://arcyton.com).

**Last Updated:** August 13, 2026

# Actor input Schema

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

One non-empty country, region, city, town, or district query. Nominatim resolves exactly one match; ambiguous or missing places can resolve unexpectedly or return no rows.

## `days` (type: `integer`):

Optional forecast length from 1 to 16 days. Runtime uses 3 when omitted. This value is ignored when both start\_date and end\_date are supplied.

## `start_date` (type: `string`):

Optional range start. Provide end\_date too. Accepts YYYY-MM-DD or a past-relative value such as '30 days', '2 weeks', '1 month', or '1 year'; month means 30 days and year means 365 days from the Actor's local date.

## `end_date` (type: `string`):

Optional range end. Provide start\_date too. Accepts the same absolute or past-relative syntax and must be on or after the resolved start. The range has no length limit: both endpoints are inclusive, so one day returns 1 row and thirty years returns 10,958.

## Actor input object example

```json
{
  "location": "Tokyo, Japan",
  "days": 3
}
```

# Actor output Schema

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

Open the daily Dataset rows. Forecasts return 1–16 rows; paired date ranges can return up to 367 inclusive rows. Actor Start can be charged even when geocoding or upstream retrieval produces zero rows.

# 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 = {
    "location": "Tokyo, Japan",
    "days": 3,
    "start_date": "",
    "end_date": ""
};

// Run the Actor and wait for it to finish
const run = await client.actor("agentx/weather-forecast-api").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 = {
    "location": "Tokyo, Japan",
    "days": 3,
    "start_date": "",
    "end_date": "",
}

# Run the Actor and wait for it to finish
run = client.actor("agentx/weather-forecast-api").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 '{
  "location": "Tokyo, Japan",
  "days": 3,
  "start_date": "",
  "end_date": ""
}' |
apify call agentx/weather-forecast-api --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,agentx/weather-forecast-api"
        }
    }
}

```

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/Yn1ksjorQhCtBcLN6/builds/jPpdMb45VaTz9aQUy/openapi.json