Website Tech, SEO & llms.txt Check for Agents

Fairquery Apify Actor that returns one structured row per website: tech stack, title/meta/Open Graph/JSON-LD, an SEO issue list with a 0–100 score, robots.txt rules for AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot, …), XML sitemap, llms.txt / llms-full.txt, security headers, and a 0–100 AI-readiness score. Plain HTTP (no browser), so it's fast and cheap.

When to use this Actor (LLM / tool selection)

Use it when an agent needs to:

• Enrich a lead/company list with "what is this site built on?" (CMS, framework, analytics, chat, payments, CDN, hosting)

• Run a quick on-page SEO check of homepages or landing pages

• Answer "does this site have an llms.txt?" / "does it block GPTBot or ClaudeBot?"

• Get a site's title, description, social links, feeds and structured-data types before summarizing it

Do not use it for full-site crawls, JS-rendered content (single-page apps may show THIN_CONTENT), Core Web Vitals, or backlink data.

Related MCP server: AI Tooling Detector MCP Server

How it works

Per site (max 3 in parallel): GET /robots.txt → GET <url> → sitemap (from robots Sitemap: or /sitemap.xml, /sitemap_index.xml) → /llms.txt and /llms-full.txt. Identifies as FairqueryBot/1.0; by default skips pages that robots.txt disallows for generic crawlers. Tech detection uses ~75 HTML/header/cookie/meta-generator signatures from a single response.

Input

{ "urls": ["stripe.com", "https://www.example.org/pricing"], "checkSitemap": false }

Output

Every row has schema_version (1.0.0), type (site | error), source_url (final fetched URL), checked_at.

{
  "type": "site", "domain": "vercel.com", "status_code": 200, "https": true, "response_ms": 310,
  "title": "Agentic Infrastructure - Vercel", "meta_description": "…", "canonical": "https://vercel.com/", "lang": "en",
  "h1": ["…"], "word_count": 256, "open_graph": { "title": "…", "image": "…" }, "json_ld_types": ["Organization", "WebSite"],
  "technologies": [{ "name": "Next.js", "category": "JS framework", "evidence": "html \"/_next/static\"" }, { "name": "Vercel", "category": "Hosting", "evidence": "header x-vercel-id" }],
  "security_headers": { "strict_transport_security": true, "content_security_policy": true },
  "robots_txt_found": true, "ai_bots": { "GPTBot": "partial", "ClaudeBot": "partial" },
  "sitemap_found": true, "sitemap_url_count": 8787, "llms_txt_found": true, "llms_txt_title": "Vercel",
  "seo_score": 100, "seo_issues": [], "ai_readiness_score": 85
}

• ai_bots[bot]: allowed (no rules), partial (some paths disallowed), blocked (root disallowed).

• seo_issues[]: {code, severity, message}; codes include NO_HTTPS, MISSING_TITLE, TITLE_TOO_LONG, MISSING_META_DESCRIPTION, MISSING_H1, MULTIPLE_H1, MISSING_CANONICAL, MISSING_LANG, MISSING_VIEWPORT, NOINDEX, MISSING_OPEN_GRAPH, MISSING_STRUCTURED_DATA, IMAGES_MISSING_ALT, THIN_CONTENT, MISSING_ROBOTS_TXT, MISSING_SITEMAP. Score = 100 − 15/8/3 per high/medium/low issue.

• ai_readiness_score: llms.txt 25, llms-full.txt 5, AI bots allowed up to 20, JSON-LD 15, ≥150 words of server-rendered text 15, sitemap 10, meta description 10; −30 if noindex.

Error rows (machine-readable, never charged)

{ "type": "error", "code": "BLOCKED_OR_RATE_LIMITED", "message": "GET https://x.com/ returned HTTP 403.", "retryable": false, "status_code": 403, "input": "x.com" }

Codes: INPUT_EMPTY, INVALID_URL, MAX_SITES_EXCEEDED, ROBOTS_DISALLOWED, FETCH_FAILED, HTTP_ERROR, BLOCKED_OR_RATE_LIMITED.

Run summary

Key-value store record RUN_SUMMARY: sites checked, errors, HTTP request count, average SEO and AI-readiness scores, llms.txt count, echoed input.

Pricing

Pay per event: site-checked per site row. Error rows are never charged. See the Store pricing tab.

Memory / runtime

Default 256 MB, no browser, no proxy. Typically 1–3 s per site.

MCP / Apify tools

Call from agents via Apify MCP (https://mcp.apify.com, authenticated with your Apify token), Actor fairquery/website-tech-seo-ai-readiness:

{ "actor": "fairquery/website-tech-seo-ai-readiness", "input": { "urls": ["stripe.com"] } }

Read dataset items (type == "site") and RUN_SUMMARY. A standalone stdio MCP server lives in the GitHub repo under mcp/.

Local stdio MCP server

This repo ships a stdio MCP server (TypeScript, @modelcontextprotocol/sdk) under mcp/ exposing one tool, check_websites, which runs this Actor through the Apify API. initialize and tools/list work without a token; tool calls require APIFY_TOKEN (billed per the Store pricing).

cd mcp && npm install && npm run build
node scripts/smoke-test.mjs                          # initialize + tools/list, no token
APIFY_TOKEN=... node scripts/smoke-test.mjs --call   # real tool call

{ "mcpServers": { "fairquery-web": { "command": "node", "args": ["/path/to/website-tech-seo-ai-readiness/mcp/dist/index.js"], "env": { "APIFY_TOKEN": "<your Apify token>" } } } }

Docker: docker build -f Dockerfile.mcp -t fairquery-web-mcp . (build context = repo root; also the Dockerfile for Glama). glama.json lists fairquery as maintainer.

Links

• Apify Store: https://apify.com/fairquery/website-tech-seo-ai-readiness

• License: MIT

Local development

npm install
npx apify run      # uses input defaults, or storage/key_value_stores/default/INPUT.json
npm test

Kits & templates

Optional guides from Fairquery for wiring this Actor into agents and automations (the Actor works fine without them):

• AI Agent Data Toolkit Quick-Start (PDF): MCP setup for Claude Desktop and Cursor, API calls, recipes, cost estimates.

• n8n + Zapier Workflow Template Pack: 10 importable n8n workflows for the four Fairquery Actors, plus Zapier setup guides.

• Agent Prompt Pack for Research & Lead Ops: 69 prompts for company research, lead cleaning, SEO and security reviews.

Disclaimer

Detection is heuristic (single request, no JS execution). Only publicly served files are read; robots.txt is respected by default.