# URL to Clean Markdown — for AI Agents (`great_saint/url-to-markdown`) Actor

Convert any web page into clean, LLM-ready Markdown. Charged only on successful conversion. MCP & x402 ready.

- **URL**: https://apify.com/great_saint/url-to-markdown.md
- **Developed by:** [Öge](https://apify.com/great_saint) (community)
- **Categories:** Agents, AI, Automation
- **Features:** Limited permissions
- **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 pages

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## URL to Clean Markdown — for AI Agents

Give it a web page URL, get back **clean, LLM-ready Markdown** — main content only, no nav/ads/boilerplate. Built for **AI agents**, RAG pipelines and research tools.

### When to use this tool

Call this whenever an agent needs to **read a web page as text**: feeding a page into an LLM, building a RAG corpus, summarizing an article, or extracting documentation. It strips the page down to its real content (via Mozilla Readability) and returns Markdown.

### Input

```json
{ "urls": [{ "url": "https://en.wikipedia.org/wiki/Model_Context_Protocol" }], "maxItems": 100 }
```

- `urls` — one or more page URLs (also accepts plain strings)
- `maxItems` — cap on pages per run (default 100)
- `proxy` — route through Apify Proxy to reduce blocks (default: on)

### Output — one dataset item per page

```json
{
  "url": "https://en.wikipedia.org/wiki/Model_Context_Protocol",
  "title": "Model Context Protocol",
  "byline": null,
  "excerpt": "An open standard for connecting AI assistants to tools and data...",
  "lang": "en",
  "wordCount": 1240,
  "markdown": "# Model Context Protocol\n\n...",
  "scrapedAt": "2026-07-14T12:00:00.000Z"
}
```

### Pricing — charge on success

**Pay per result.** You are charged **only for pages successfully converted to Markdown**. If a page is blocked or has no readable content, nothing is pushed and **nothing is charged**.

### Notes

- Content extraction via Mozilla Readability + Turndown (same engine family as Firefox Reader View).
- Apify Proxy included to reduce blocking.
- **MCP & x402 compatible** — callable directly by AI agents, no API key required via x402.
- Returns clean Markdown ready to drop straight into an LLM context window.

# Actor input Schema

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

Web page URLs to convert into clean, LLM-ready Markdown.

## `maxItems` (type: `integer`):

Maximum number of pages to process this run.

## `proxy` (type: `boolean`):

Route requests through Apify Proxy to reduce blocks. Recommended.

## Actor input object example

```json
{
  "urls": [
    {
      "url": "https://en.wikipedia.org/wiki/Model_Context_Protocol"
    }
  ],
  "maxItems": 100,
  "proxy": true
}
```

# 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": [
        {
            "url": "https://en.wikipedia.org/wiki/Model_Context_Protocol"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("great_saint/url-to-markdown").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": [{ "url": "https://en.wikipedia.org/wiki/Model_Context_Protocol" }] }

# Run the Actor and wait for it to finish
run = client.actor("great_saint/url-to-markdown").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 '{
  "urls": [
    {
      "url": "https://en.wikipedia.org/wiki/Model_Context_Protocol"
    }
  ]
}' |
apify call great_saint/url-to-markdown --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,great_saint/url-to-markdown"
        }
    }
}
```

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/kGfwySv4arhRschHF/builds/fryCIquU0hhFBFaGa/openapi.json