Read a site's robots.txt and say which AI crawlers may fetch it, separating the ones that retrieve pages to answer live questions from the ones that only collect training data. Blocking the first kind is what makes a site invisible in AI answers; blocking the second costs nothing. Runs locally, no account needed.

Probe a site for the identity files an AI engine may look for — llms.txt, ai.json, identity.json and the rest — and report which exist. Runs locally, no account needed.

Read a site's homepage and report the signals an engine uses to work out what it is: title, description, canonical, hreflang and the Schema.org types in its JSON-LD. Runs locally, no account needed.

Free and instant, no account: read a website the way an AI engine does before it ever answers about it. Returns which answer-engine crawlers robots.txt lets in (retrieval, which decides whether the site can be cited) and which training crawlers it blocks (which costs nothing), the identity files present (llms.txt, ai.json and the rest), whether the homepage carries Organization schema, an identity score out of 100, and the fixes that reading names. No model is asked; for whether the engines actually name the site, run analyze_site.

The AI visibility audit: the identity files, crawler policy, schema graph and page health, then the answer engines asked whether they name the site, scored out of 100. Starts a run and returns its run_id; it takes about a minute, so call get_analysis with that id. Without an account it runs the survey (the audit without its two paid steps), two a day per address; a free account gets the teaser, a paid plan the whole audit, on the account's own allowance.

Read an audit started by analyze_site: progress while it runs, then the score out of 100 and what to fix first. Only the caller who started the run can read it - the account, or for a guest the same address.

The fix plan for a site's AI visibility, most important first: what to change, why it matters for being named in AI answers, how many points of the score it should recover, the pages it applies to, and a paste-ready file or snippet when there is one (llms.txt, robots.txt rules, Organization schema). Free from the identity reading, no account; signed in, it is built from your latest finished audit of that site, which covers the answer engines too.

Your site against a competitor's, read the way an AI engine reads them before it answers: identity score out of 100, which answer-engine crawlers each lets in, the identity files each has (llms.txt, ai.json and the rest), Organization schema, and where each one is ahead. Free and instant, no model asked; counts two identity checks.

How a site's AI visibility reading has moved over time: for a site in the INITE Atlas, its weekly readings (identity score, answer-engine crawlers let in, llms.txt, Organization schema, the top fix each week); signed in, your own audits of it too, with their scores. Free, no model, and no check is spent: it reads what was already measured.

INITE's MCP servers and how to add each to this client: INITE Atlas and INITE Atlas Owners (signed in: add a tool, its card, premium), INITE Visibility and its signed-in door (your audits, a licence), INITE and its signed-in door (your account, Local Start). Call it when the person wants something this server does not do; give for to get the one server that does it. Returns the URL, the Claude Code command, Cursor and VS Code install links, and the custom-connector step for claude.ai and ChatGPT.