, Qomvia
Qomvia is live on Product Hunt and Sell With Boost
Qomvia launched on Product Hunt on 23 September 2026 and is now listed on Sell With Boost. What we launched and where to find us.
Insights
What we learn measuring agent readiness across the index: which blocks cost the most answers, which markup actually gets quoted, and what changes when a model rather than a person is the reader.
, Qomvia
Qomvia launched on Product Hunt on 23 September 2026 and is now listed on Sell With Boost. What we launched and where to find us.
, Qomvia
Well-known files only work once an agent is already talking to your server. Two newer efforts move discovery earlier, into DNS and into searchable registries. What they are and how far along they are.
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Two young formats give agents more than text: Skills are folders of instructions an agent can load, WebMCP turns page forms and functions into callable tools. What each is for and how to publish them.
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User-agent strings can be forged and IP lists go stale. Web Bot Auth lets an agent sign each request with a key you can verify. What it is, what you can do about it today, and what Qomvia looks for.
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An agent that hits a 401 needs to know what to do next. Two RFCs and one Markdown convention give it a machine-readable answer instead of a login page it cannot use.
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A feed is how a monitoring agent notices you changed. An oEmbed endpoint is how another surface renders you with the credit attached.
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Agents find APIs the way they find everything else: by probing a well-known path. RFC 9727 defines that path and the linkset format that goes in it. Publishing one takes an afternoon.
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Building an MCP server is half the job. The other half is a small JSON document at a well-known path so a tool-calling assistant can find the server without a human pasting a URL.
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Disallowing GPTBot does not protect the content that is already public. It removes the site from the surface where the buying question now gets asked.
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An agent should not have to parse your HTML to find your API, your llms.txt or your MCP server. An RFC 8288 Link header on the HTTP response tells it in one line.
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robots.txt can only say who may fetch. Content Signals add what they may do with it afterwards: search, AI input, AI training. One line, three switches, and a legal reservation of rights.
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Four questions decide whether an assistant can use a website: can it fetch it, read it, trust it and act on it. Everything in the rubric hangs off one of them.
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An agent that sends Accept: text/markdown is asking for your page without the HTML. Answering costs you a few lines of config and saves the agent most of its tokens.
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An agent platform that wants to know what you publish reads one file. If that file is missing, undeclared or stale, it learns your site by luck, and luck skips most pages.
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llms.txt is a reading list for models, not a second sitemap. The format has four parts, the useful file fits on one screen, and the most common mistake is dumping every URL you own into it.
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An assistant answering in German needs to know which of your pages is the German one. Two attributes tell it. Most multilingual sites set neither correctly.
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Answer engines prefer sources they can date. A page with no machine-readable date loses time-sensitive questions to a competitor that carries one, even when yours is newer.
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When an answer engine credits a source, it needs a label and an address. The title is the label, the canonical URL is the address. Get either wrong and the credit goes astray.
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Answer engines do not read pages, they read passages. Headings decide where a passage begins and what context it keeps. Bad headings produce quotes that mean something else.
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Before a model reads your page, an extractor throws most of it away. Landmarks tell it what to keep. Without them, the answer quotes your cookie banner.
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An agent has no email address, no card and no patience for a registration form. Anything behind one does not exist to it. What to leave public so the site is still quotable.
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When an agent operator needs to reach you, or a platform needs to know whether it may act on your site, both look for a file at a fixed address. Most sites have neither. Both take fifteen minutes.
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An AI agent fetching your page reads the HTML the server sends. If that HTML is a script tag and an empty div, the agent reads nothing, and no amount of good content fixes it.
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Challenge pages, JavaScript checks and 403s are what an AI agent sees on many well-run sites. How to let declared agents through without opening the door to scrapers.
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An agent does not wait for your page to paint. It waits for bytes, then parses them. Two numbers decide whether it gives up, and neither is the Core Web Vitals score you already track.
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Every AI platform runs three different robots against your site. Knowing which is which is the difference between opting out of training and opting out of being an answer.
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The nine user agents that decide whether ChatGPT, Claude, Perplexity and Gemini can read your site, what each one controls, and a robots.txt that allows answers without allowing training.
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A model that has read about your brand on ten sites still has to decide whether your domain is that brand. One block of structured data on the homepage settles it.
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An assistant can mention your brand from memory, or link your page as its source. Only one of these sends a visitor. Here is what separates them and which signals move each.