For developers and AI

Scan for AI visibility from code, or from Claude.

Everything the web scanner does is available two ways: a small REST API for your own scripts and products, and an MCP server so assistants like Claude and ChatGPT can scan websites for you as a tool. Same pipeline, same evidence-based report.

Get an API key

Access uses a key (a Bearer token that looks like lantad_…) with a daily scan cap, because every scan runs a real browser render. Any paid plan that includes the API issues your key automatically: it is on your account dashboard the moment you subscribe (Pro Monitor from $29/mo, Business from $99/mo). Send the key as an Authorization: Bearer header; never put it in a URL or commit it to a repo. Need higher limits or a custom arrangement? Email [email protected].

REST API

Base URL https://lantad.co. All requests and responses are JSON.

Start a scan

curl -X POST https://lantad.co/api/v1/scan \
  -H "Authorization: Bearer lantad_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"url": "yoursite.com"}'

A bare domain works; no scheme needed. Returns 202 with a scan_id, a report_key, and the URLs to poll and to fetch the result:

{
  "scan_id": "1c2fecfa-…",
  "report_key": "noYTkhb0v3cTrpBPo7hpK",
  "status": "queued",
  "report_url": "https://lantad.co/r/noYTkhb0v3cTrpBPo7hpK",
  "status_url": "https://lantad.co/api/v1/scan/1c2fecfa-…",
  "result_url": "https://lantad.co/api/v1/report/noYTkhb0v3cTrpBPo7hpK"
}

Check status

curl https://lantad.co/api/v1/scan/SCAN_ID

Returns { "status": "queued" | "running" | "done" | … } plus the sub-scores once the grade lands. Scans usually finish in under a minute.

Get the full report

curl https://lantad.co/api/v1/report/REPORT_KEY

Returns the complete structured verdict: the AI Visibility Score and grade, Prose Parity and the other sub-scores, the per-bot access matrix for all checked crawlers, the ranked defects with their fixes, and the evidence behind each. The report_key is the capability, so this endpoint needs no key, exactly like the shareable web report.

Limits and behaviour

A repeat scan of the same URL inside 24 hours returns the existing report and does not use budget.
Each key has a daily scan cap, resetting at 00:00 UTC.
The same SSRF and safety guards as the web form apply: private, reserved, and non-HTTP targets are refused.

Bulk sitemap audit (paid plans)

curl -X POST https://lantad.co/api/v1/bulk \
  -H "Authorization: Bearer lantad_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"sitemap": "https://yoursite.com/sitemap.xml"}'

Audits every same-site page in the sitemap with the raw-HTML checks (no browser render): readability verdict, structure facts, structured data, and per-crawler robots access. Poll GET /api/v1/bulk/ID for progress; results include a shareable page and a per-page CSV. Included with Pro Monitor (up to 1,000 pages per job, 2,000 per month); larger audits are part of the Deep Audit service.

MCP server (Claude, ChatGPT, and other assistants)

Lantad runs an MCP server so an assistant can scan a site for you and read the result back. It speaks Streamable HTTP JSON-RPC at:

https://lantad.co/api/mcp

Authenticate with the same Bearer key. Two tools are exposed:

scan_website(url) runs a scan and waits up to about 22 seconds for the graded result, returning the score, grade, sub-scores, top fixes, and a report link.
get_scan_report(report_key) fetches a prior report, useful when a slow site did not finish inside the wait window.

Connect it

In an MCP client that supports remote HTTP servers with headers (for example Claude Desktop or Cursor), add:

{
  "mcpServers": {
    "lantad": {
      "url": "https://lantad.co/api/mcp",
      "headers": { "Authorization": "Bearer lantad_YOUR_KEY" }
    }
  }
}

In Claude or ChatGPT's custom connector settings, add a connector pointing to https://lantad.co/api/mcp and set the Authorization header to your key. Then ask the assistant to scan a site, for example: "Scan stripe.com for AI visibility and summarise the top fixes."

Questions, higher limits, or a bug: [email protected]. The method behind the numbers is on the methodology page.