Agent-readiness guides

These short guides explain, in plain language, what makes a website or an API easy for AI agents to read and use. Each one covers a single topic and takes a few minutes to read. They are free, and they cover the same surfaces an agent-readiness audit measures.

The first guide explains what an agent-readiness audit is.

Discovery and content

How an agent finds your site and reads it without getting lost.

Capability and trust

How a site tells an agent what it is allowed to do, and shows it is safe to use.

Commerce and strategy

Paying agents, how this differs from SEO, and how to choose and measure an audit.

Frequently asked

What is an agent-readiness audit?

An agent-readiness audit measures how well an AI agent can discover, read, and act on a website or an API, scored against current standards by an independent scanner rather than a self-assessment.

Do I need llms.txt on my site?

If you want models and agents to read your real content rather than guess from a cached snippet, llms.txt gives them a curated map of what matters. It does not replace robots.txt or a sitemap, it complements them.

How do I get my site cited by AI assistants?

A model cites content it can read cleanly and corroborate. That means machine-readable surfaces such as llms.txt and structured data, a markdown form that does not exhaust the token budget, and being indexed where the assistant searches.

What is an MCP server card?

An MCP server card is a JSON file, usually at /.well-known/mcp/server-card.json, that lets an agent discover a site's Model Context Protocol server and the tools it exposes, so the agent can call them without a human wiring up the connection.

Is agent-readiness the same as SEO?

No. SEO makes a site rank for a person to click. Agent-readiness makes a site legible and usable by an agent that reads and acts. A site can rank well and still be opaque to agents.

How is agent-readiness measured?

By an independent scanner that reads the live site and reports a score with a category breakdown. The categories that get fixed read higher on the next scan, so the claim is the number rather than an assertion.

For an audit, contact info@turva.dev.