# LLMs.txt / AI Readiness Auditor (`civicdataworks/llms-txt-ai-readiness-auditor`) Actor

Audit websites for AI crawler/readiness basics: llms.txt, sitemap, robots rules, titles, metadata, and AI crawler directives.

- **URL**: https://apify.com/civicdataworks/llms-txt-ai-readiness-auditor.md
- **Developed by:** [Rowan Mercer](https://apify.com/civicdataworks) (community)
- **Categories:** Marketing, Business
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$20.00 / 1,000 returned dataset rows

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

## LLMs.txt / AI Readiness Auditor

Audit websites for AI crawler/readiness basics: llms.txt, sitemap, robots rules, titles, metadata, and AI crawler directives.

### Who this is for

AI SEO teams, SaaS marketers, and agencies.

### What it returns

one readiness audit record per site.

### Inputs

website root URLs.

### Billing

This actor is designed for Apify pay-per-event pricing. It charges one `result-item` event for each dataset row successfully returned, subject to the customer's max charge limit.

Launch price: `$0.02` per returned result.

### Boundaries

- Uses public pages, public feeds, or public APIs only.
- Does not bypass logins, CAPTCHAs, paywalls, or source access controls.
- Does not provide legal, compliance, security, procurement, or investment advice.
- Source terms, robots policy, and API limits are treated as hard operational boundaries; this actor does not bypass access controls.

# Actor input Schema

## `siteUrls` (type: `array`):

Website roots to audit, for example https://example.com.

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

Maximum result records returned by this run.

## Actor input object example

```json
{
  "maxItems": 100
}
```

# 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("civicdataworks/llms-txt-ai-readiness-auditor").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("civicdataworks/llms-txt-ai-readiness-auditor").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 '{}' |
apify call civicdataworks/llms-txt-ai-readiness-auditor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,civicdataworks/llms-txt-ai-readiness-auditor"
        }
    }
}
```

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/1efM9Hyt7G9pxtt8W/builds/LTGV8AB33NBP73vYZ/openapi.json