# LLM-Ready Web Extractor (`phantom_horse/my-actor-1`) Actor

Turn any web page into clean, LLM-ready Markdown. Strips scripts, nav, and page chrome, then converts the main content to tidy Markdown with title, meta description, and token counts. Perfect for AI prompts and RAG ingestion pipelines.

- **URL**: https://apify.com/phantom\_horse/my-actor-1.md
- **Developed by:** [NATNAEL FIKRE](https://apify.com/phantom_horse) (community)
- **Categories:** AI, Automation, Developer tools
- **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 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

## LLM-Ready Web Extractor

Turn any web page into clean, structured **Markdown that is ready to paste into an LLM prompt** or pipe into a RAG ingestion pipeline.

### What it does

- Fetches one or more URLs (optionally crawling same-domain subpages)
- Strips scripts, styles, navigation, headers, footers, and other page chrome
- Picks the semantic content container (`<main>` or `<article>`) when available
- Converts the content to tidy Markdown (ATX headings, fenced code blocks)
- Outputs one dataset item per page with the Markdown, title, meta description, character count, and an approximate token count

### Why

Raw HTML wastes tokens and confuses models. This Actor gives you compact, structured text - typically 5-10x smaller than the original HTML - so your prompts stay cheap and your retrieval stays clean.

### Input

| Field | Type | Description |
|---|---|---|
| `startUrls` | array | Pages to extract |
| `crawlSubpages` | boolean | Follow same-domain links (default: false) |
| `maxPages` | integer | Max pages to process (default: 10) |

### Output

Each dataset item contains: `url`, `title`, `description`, `markdown`, `characterCount`, `approxTokens`, and `extractedAt`.

### Typical uses

- Feeding documentation or articles to Claude / other LLMs without HTML noise
- Building RAG corpora from websites
- Scheduled content monitoring in LLM-friendly format

# Actor input Schema

## `startUrls` (type: `array`):

Pages to convert into clean, LLM-ready Markdown.

## `crawlSubpages` (type: `boolean`):

Also follow same-domain links found on the start URLs (up to Max pages).

## `maxPages` (type: `integer`):

Maximum total number of pages to process.

## Actor input object example

```json
{
  "startUrls": [
    {
      "url": "https://docs.apify.com/platform"
    }
  ],
  "crawlSubpages": false,
  "maxPages": 10
}
```

# Actor output Schema

## `results` (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 = {
    "startUrls": [
        {
            "url": "https://docs.apify.com/platform"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("phantom_horse/my-actor-1").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 = { "startUrls": [{ "url": "https://docs.apify.com/platform" }] }

# Run the Actor and wait for it to finish
run = client.actor("phantom_horse/my-actor-1").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 '{
  "startUrls": [
    {
      "url": "https://docs.apify.com/platform"
    }
  ]
}' |
apify call phantom_horse/my-actor-1 --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=phantom_horse/my-actor-1",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/ieGkK3W50nGZEjpym/builds/35eSlZGXyGYgX4gdh/openapi.json