# JP Article Extractor — 日本語記事メタデータ抽出 (`sunsun0616/jp-article-extractor`) Actor

日本語記事(note/Zenn/ブログ/ニュース)のURLからtitle/description/og:image/公開日/本文冒頭を構造化JSONで抽出。og/meta/JSON-LD/titleタグの多段フォールバックと日本語日付表記の正規化に対応。

- **URL**: https://apify.com/sunsun0616/jp-article-extractor.md
- **Developed by:** [駿 西嶋](https://apify.com/sunsun0616) (community)
- **Categories:** News, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $5.00 / 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 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

## JP Article Extractor — Japanese Article Metadata Extraction

Extract structured metadata from Japanese article URLs (news sites, blogs, note, Zenn, and more). Built specifically for Japanese-language content, including normalization of Japanese date formats.

日本語記事（ニュース / ブログ / note / Zenn など）の URL リストから、メタデータを構造化 JSON で抽出します。和文日付表記の正規化に対応した、日本語コンテンツ特化の Actor です。

### What it does

Give it a list of URLs, and for each page it returns:

| Field | Description |
|---|---|
| `title` | Article title (og:title → JSON-LD → `<title>` fallback chain) |
| `description` | Meta description |
| `og_image` | Social share image URL |
| `published_at` | Publication date, normalized to ISO format — including Japanese formats like `2026年7月3日` → `2026-07-03` |
| `excerpt` | First ~200 characters of body text |

Failed URLs return `{"url": "...", "error": "..."}` without stopping the batch.

### Why Japanese-specific?

Generic article extractors often fail on Japanese sites:

- **Japanese date formats** (`2026年7月3日`, `2026/7/3` etc.) are normalized to ISO `YYYY-MM-DD`
- **Character encoding detection** handles UTF-8 and Shift\_JIS (still common on Japanese sites) automatically
- Multi-stage fallback (Open Graph → JSON-LD → meta tags → title tag) tuned against Japanese news sites, blogs, note and Zenn

### Input

```json
{
  "urls": [
    "https://example.com/article-1",
    "https://example.com/article-2"
  ]
}
```

### Output (dataset, one record per URL)

```json
{
  "url": "https://note.com/x/n/1",
  "title": "記事タイトル",
  "description": "ディスクリプション",
  "og_image": "https://example.com/og.png",
  "published_at": "2026-07-01",
  "excerpt": "本文冒頭200字..."
}
```

### Use cases

- Media monitoring dashboards for Japanese news and blogs
- Enriching link lists with titles, dates and thumbnails (newsletters, curation sites)
- Feeding Japanese article metadata into LLM / RAG pipelines
- Content research on note / Zenn / Japanese tech blogs

### Works well with

Japanese news sites, personal and corporate blogs (WordPress, はてなブログ), note.com, Zenn, and most pages that expose Open Graph, JSON-LD, or standard meta tags. It is a lightweight alternative to a full Japanese web scraper when you only need article metadata — no headless browser, no proxies required for most public pages.

### Pricing example

Pay-per-event pricing, charged only for what you extract:

- 100 articles ≈ **$0.50**
- 1,000 articles ≈ **$5.00**

(Plus a small Actor start fee and Apify platform usage — kept low by the 1 GB default memory.)

### FAQ

**Does it work with sites other than news/blogs?**
Any page with standard metadata (OG tags, JSON-LD, meta description) works — e.g. corporate press releases or product announcement pages in Japanese.

**How is this different from generic article extractors?**
Japanese date normalization (`2026年7月3日` → `2026-07-03`), Shift\_JIS support, and fallback order tuned for Japanese sites (note, Zenn, national news media). Generic extractors often return empty dates or garbled text on these.

**Can I use it from the API / integrate it into a pipeline?**
Yes — call it via the Apify API or scheduled runs, and read results from the dataset as JSON/CSV. Works as a building block for media monitoring, newsletters, and LLM/RAG pipelines.

**日本語で使えますか？**
はい。入力は URL のリストだけです。日本語記事のタイトル・説明・OGP画像・公開日（ISO 形式に正規化）・冒頭抜粋を JSON で返します。

### Notes

- Lightweight and fast: standard-library HTTP fetching, no headless browser
- Runs at 1 GB memory by default to keep your platform usage cost low
- Pages that block simple HTTP clients may return an `error` record

# Actor input Schema

## `urls` (type: `array`):

抽出対象の記事URLリスト

## Actor input object example

```json
{
  "urls": [
    "https://example.com/article"
  ]
}
```

# Actor output Schema

## `articles` (type: `string`):

Structured metadata records (title, description, og\_image, ISO-normalized published\_at, excerpt) for each input URL, stored in the default dataset.

# 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 = {
    "urls": [
        "https://example.com/article"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("sunsun0616/jp-article-extractor").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 = { "urls": ["https://example.com/article"] }

# Run the Actor and wait for it to finish
run = client.actor("sunsun0616/jp-article-extractor").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 '{
  "urls": [
    "https://example.com/article"
  ]
}' |
apify call sunsun0616/jp-article-extractor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=sunsun0616/jp-article-extractor",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/TcoQtmvVKJC6rcYS9/builds/tB0PbhUW65CVQrfcw/openapi.json