# AI Access Audit (robots.txt + llms.txt) (`conserving_mastodon/ai-access-audit`) Actor

How does a site present itself to AI? Per-crawler robots.txt verdicts for 13 major AI user agents (GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot and more), llms.txt presence and shape, sitemap signals, and a concrete fix list. Charged only when the audit completes.

- **URL**: https://apify.com/conserving\_mastodon/ai-access-audit.md
- **Developed by:** [Chris Arsenault](https://apify.com/conserving_mastodon) (community)
- **Categories:** SEO tools, AI, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$250.00 / 1,000 completed audits

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

## AI Access Audit (robots.txt + llms.txt)

**How does a site present itself to AI?** In one call: a per-crawler verdict for the 13 AI user agents that matter, llms.txt presence and shape, sitemap discoverability, and a concrete fix list. This is the audit behind every "are we visible to AI?" conversation happening in marketing and SEO teams right now.

### What it checks

- **robots.txt policy, per AI crawler**: GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, Claude-Web, anthropic-ai, PerplexityBot, Perplexity-User, Google-Extended, Applebot-Extended, CCBot, Bytespider, meta-externalagent. Each gets a verdict — `blocked`, `partial`, `allowed`, or `unaddressed` — plus the exact rule that decides it. Training crawlers and on-behalf-of-user agents are labeled by purpose, because blocking one and not the other is usually a deliberate choice.
- **llms.txt**: present or not, and whether it has the shape consumers expect (sections, markdown links) or is just a stub.
- **Sitemap**: declared in robots.txt, and reachable.
- **Recommendations**: a short, specific fix list — never generic advice.

### Output

A summary row plus one row per crawler:

```json
{ "agent": "GPTBot", "purpose": "OpenAI training", "verdict": "unaddressed",
  "basis": "no applicable rules (crawlers default to allowed)" }
```

### Why "unaddressed" matters

Most sites have never stated an AI policy at all — which means every AI crawler assumes full access. Whether that is fine or a problem is a business decision; this audit makes the current state explicit so someone can actually decide.

### Use cases

- **Agencies**: the AI-visibility line item for every site audit, generated in seconds.
- **Publishers deciding an AI stance**: see the current posture before changing it, then re-run to verify the change took.
- **Agents doing due diligence**: check whether a data source permits AI access before building on it.

Billing is per completed audit; three or four polite GETs per run. Built by 1450 Enterprises, the team behind the WordPress Content Audit and MCP Server Probe.

# Actor input Schema

## `url` (type: `string`):

Root URL of the site to audit (e.g. https://example.com).

## Actor input object example

```json
{
  "url": "https://anthropic.com"
}
```

# Actor output Schema

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

First row: robots.txt/llms.txt/sitemap posture with recommendations. Subsequent rows: each AI user agent with its verdict (blocked, partial, allowed, unaddressed) and the rule that decides it.

# 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 = {
    "url": "https://anthropic.com"
};

// Run the Actor and wait for it to finish
const run = await client.actor("conserving_mastodon/ai-access-audit").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 = { "url": "https://anthropic.com" }

# Run the Actor and wait for it to finish
run = client.actor("conserving_mastodon/ai-access-audit").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 '{
  "url": "https://anthropic.com"
}' |
apify call conserving_mastodon/ai-access-audit --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,conserving_mastodon/ai-access-audit"
        }
    }
}

```

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/dTqot8jqyt3CIiSwh/builds/Y1d46UvLgUGw5TiXH/openapi.json