# AI Agent Readiness Scanner: llms.txt, AI Bots, MCP & A2A (`kryptoryn/agentic-web-readiness-scanner`) Actor

Score any website 0-100 for AI search and AI agents: llms.txt, robots rules for 18 AI bots (GPTBot, ClaudeBot, PerplexityBot...), Content-Signal, JSON-LD, no-JS readability, MCP servers, A2A agent cards, AI Catalog, OpenAPI, WebMCP. Bulk + fixes.

- **URL**: https://apify.com/kryptoryn/agentic-web-readiness-scanner.md
- **Developed by:** [Manuel P.](https://apify.com/kryptoryn) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $8.00 / 1,000 website scanneds

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

## AI Agent Readiness Scanner

**Is your website ready for AI agents and AI search?** This Actor scores any website from 0 to 100 on how well it can be found, read and used by ChatGPT, Claude, Perplexity, Gemini and autonomous agents, and tells you exactly what to fix first.

Classic SEO checks don't cover the **agentic web**. This Actor checks the new standards:

- **llms.txt / llms-full.txt**: present, well formed (H1, summary, sections, links)
- **AI crawler access**: robots.txt rules for 18 AI bots, grouped by purpose: *training* (GPTBot, ClaudeBot, Google-Extended, CCBot...), *AI search* (OAI-SearchBot, Claude-SearchBot, PerplexityBot...) and *user-triggered fetchers* (ChatGPT-User, Claude-User, Perplexity-User...)
- **Content-Signal** (`search`, `ai-input`, `ai-train`) and `noai` meta / X-Robots-Tag
- **Content readability**: how many words are visible **without JavaScript** (most AI crawlers don't run JS), schema.org JSON-LD types, H1, language, Markdown alternates
- **Agent interfaces**: live **MCP servers** (`/mcp`, `mcp.<domain>`), MCP Server Cards, **AI Catalog** (`/.well-known/ai-catalog.json`), **A2A Agent Cards** (`/.well-known/agent-card.json`), **OpenAPI** specs, OAuth authorization server metadata, **WebMCP** (`navigator.modelContext`) and NLWeb

### Scoring

Four pillars of 25 points each: **Discoverability**, **AI Access**, **Content Readability** and **Agent Interfaces**. You get an A–F grade and a level: *Invisible to AI*, *Partially visible*, *AI-search ready* or *Agent-native*.

Every site also gets **prioritized recommendations** (high / medium / low impact) and a single **top fix**.

### Use cases

- **SEO / GEO agencies:** run bulk AI-visibility audits for prospects and clients. A bad grade is an easy pitch.
- **Lead generation:** find companies that block AI search bots or have no llms.txt, then sell them the fix.
- **Brands and publishers:** check you aren't accidentally blocking AI search while trying to block training.
- **Market research:** track how fast a sector adopts MCP, A2A and llms.txt.
- **Monitoring:** schedule it weekly and get alerts when someone changes robots.txt.

### Input

```json
{ "websites": ["stripe.com", "nytimes.com", "yourclient.com"] }
```

### Output example

```json
{
  "domain": "stripe.com",
  "grade": "B",
  "score": 76,
  "level": "Agent-native",
  "pillars": {"discoverability": 22, "aiAccess": 23, "contentReadability": 19, "agentInterfaces": 12},
  "summary": "stripe.com: B (76/100) Agent-native. llms.txt yes, AI search bots 7/7 allowed, MCP yes, A2A no, JSON-LD 7 types",
  "llmsTxt": {"present": true, "hasH1": true, "links": 120, "llmsFullTxt": false},
  "aiCrawlerPolicy": {"blockedBots": [], "byPurpose": {"train": {"allowed": 7, "total": 7}, "search": {"allowed": 7, "total": 7}}},
  "agentInterfaces": {"mcpServers": [{"url": "https://mcp.stripe.com/", "authRequired": true}], "a2aAgentCard": null, "openApi": null},
  "recommendations": [{"impact": "low", "recommendation": "Add /llms-full.txt ..."}]
}
```

There are two dataset views: **Overview** (one row per site) and **Recommendations** (one row per fix). Export to CSV, Excel or JSON, or send the results to Sheets, Slack, HubSpot, n8n, Make or Zapier.

### Pricing

Pay per event: you are charged once per website successfully scanned. Unreachable sites are free.

### How it works

The scanner makes about 15 lightweight HTTP requests per site: the homepage, robots.txt, llms.txt, well-known files and an MCP `initialize` probe. It doesn't need a browser or proxies, and it never calls MCP tools or sends form data.

### FAQ

**Should I block AI bots?** That's up to you, but block *training* bots and *search* bots separately. Blocking OAI-SearchBot or PerplexityBot means you won't be cited in AI answers.

**What is llms.txt?** A Markdown file at `/llms.txt` that gives LLMs a curated map of your site.

**What is an A2A Agent Card?** A JSON file at `/.well-known/agent-card.json` that describes an AI agent you run, as defined by the Agent2Agent protocol.

# Actor input Schema

## `websites` (type: `array`):

Domains or URLs to audit, e.g. stripe.com. Bulk lists of thousands are fine.

## `probeMcpEndpoints` (type: `boolean`):

Sends an MCP initialize request to /mcp and mcp.<domain> to detect remote MCP servers (no tools are called).

## `includePerBotDetail` (type: `boolean`):

Adds the allow/block status for each of the 18 AI crawlers and agents.

## `maxConcurrency` (type: `integer`):

How many targets to scan in parallel.

## `timeoutSecs` (type: `integer`):

Timeout for each HTTP request.

## Actor input object example

```json
{
  "websites": [
    "stripe.com",
    "nytimes.com",
    "example.com"
  ],
  "probeMcpEndpoints": true,
  "includePerBotDetail": true,
  "maxConcurrency": 10,
  "timeoutSecs": 15
}
```

# Actor output Schema

## `overview` (type: `string`):

No description

## `recommendations` (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 = {
    "websites": [
        "stripe.com",
        "nytimes.com",
        "example.com"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("kryptoryn/agentic-web-readiness-scanner").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 = { "websites": [
        "stripe.com",
        "nytimes.com",
        "example.com",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("kryptoryn/agentic-web-readiness-scanner").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 '{
  "websites": [
    "stripe.com",
    "nytimes.com",
    "example.com"
  ]
}' |
apify call kryptoryn/agentic-web-readiness-scanner --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,kryptoryn/agentic-web-readiness-scanner"
        }
    }
}
```

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/S34i2sjhcgAm6dEOn/builds/pP8N0dChcMt8KPLBJ/openapi.json