What is agent readability?

Agent readability is how well a website can be discovered, parsed, understood, and acted upon by AI agents — the machine-facing counterpart to human accessibility. It covers discovery files like llms.txt, machine-readable structured data, skill files like AGENTS.md, and protocol manifests for MCP and A2A.

What's the difference between llms.txt and AGENTS.md?

They serve different agents. llms.txt is a curated index that helps retrieval and answer-engine agents find a site's most useful content. AGENTS.md is a skill file that tells coding agents how to build, test, and work inside a codebase. Many projects ship both.

Do I need both an MCP server card and an A2A agent card?

Only if you expose those capabilities. Publish an MCP server card if your site offers tools or resources for AI clients to call; publish an A2A agent card if you run an agent that other agents discover and delegate to. A content site that just wants to be read and cited needs neither.

What is a markdown mirror and why does it matter?

A markdown mirror is a clean-Markdown copy of an HTML page, served at / .md or via content negotiation. It matters because AI extractors parse Markdown far more reliably than HTML cluttered with navigation, scripts, and styling — and several agent-readability checks look for one.

How is agent readability different from SEO?

Traditional SEO optimizes for human click-through from search rankings. Agent readability optimizes for machines that read, cite, and act — emphasizing clean structure, machine-readable manifests, structured data, and content negotiation. They overlap, but agent readability targets answer engines and autonomous agents rather than the blue-link results page.

Agent readability glossary: llms.txt, MCP, A2A & more

This glossary defines the core vocabulary of agent readability: the files, protocols, and concepts that determine whether AI agents can discover, parse, and act on a website. Each definition is deliberately concise.

The canonical references for these terms include llmstxt.org, the Model Context Protocol, the A2A protocol, and Schema.org.

What are the core agent-readability concepts?

Agent readability: The degree to which a website can be discovered, parsed, understood, and acted upon by AI agents — the machine-facing counterpart to human accessibility.
AI agent: An autonomous or semi-autonomous AI system that reads web content and calls APIs to complete tasks on a user's behalf, rather than rendering pages for a person to view.
Answer engine: An AI system such as ChatGPT, Perplexity, Claude, or Google AI Overviews that answers a query directly by synthesizing and citing sources instead of returning a list of links. Optimizing for it is called answer-engine optimization (AEO) or generative-engine optimization (GEO).
Content negotiation: An HTTP mechanism in which a client's Accept header tells the server which representation to return — for example sending Accept: text/markdown to receive a page's Markdown mirror instead of HTML.
Structured data (JSON-LD): Machine-readable metadata embedded in a page using Schema.org vocabulary in JSON-LD format, letting agents and search engines extract entities, relationships, and facts without parsing prose.
Vercel Agent Readability Spec: Vercel's specification defining the discovery files, structured data, and content conventions that make a site readable by AI agents. Agent Ready scores sites against it.
.well-known: A standardized URI prefix, defined by RFC 8615, under which a site publishes machine-readable metadata at predictable paths — for example /.well-known/mcp.json or /.well-known/agent-card.json.

What are the agent discovery and indexing files?

llms.txt: A Markdown file at a site's root that gives AI agents a curated index of its most useful pages and resources, with a short summary and categorized links. Defined by the llmstxt.org specification.
llms-full.txt: A companion to llms.txt that concatenates a site's primary content as a single Markdown document, so large-context agents can ingest everything in one request instead of crawling page by page.
AGENTS.md: A Markdown skill file, conventionally at a repository or site root, that tells coding agents how to build, test, and work within a project. A vendor-neutral convention that supersedes tool-specific files like CLAUDE.md and .cursorrules.
sitemap.md: A Markdown counterpart to sitemap.xml that lists a site's pages as a readable linked outline rather than raw XML, so agents can crawl the document tree without an XML parser.
Markdown mirror: A plain-Markdown version of an HTML page, served at /<page>.md or returned via content negotiation, so AI extractors can read clean text without parsing markup.

What are the agent protocols and cards?

Model Context Protocol (MCP): An open protocol that lets AI applications connect to external tools, resources, and prompts over a standard JSON-RPC interface. An MCP server exposes capabilities that any MCP-compatible client, such as Claude or ChatGPT, can call.
MCP server card: A JSON manifest at /.well-known/mcp.json that advertises an MCP server's metadata, transport, and capabilities so agents can discover and connect to it. Specified in SEP-1649.
Agent2Agent Protocol (A2A): An open protocol for agent-to-agent communication and capability discovery, letting independent agents find one another and delegate tasks.
A2A agent card: A JSON manifest at /.well-known/agent-card.json that describes an agent's identity, capabilities, and skills under the A2A protocol.
agents.json: A manifest, defined by Wildcard as an OpenAPI extension, that maps a REST API's endpoints to agent-callable actions so agents can invoke them reliably.
agent-permissions.json: An emerging manifest convention for declaring which actions AI agents are and are not permitted to take on a site.
NLWeb: An open Microsoft protocol that gives a website a natural-language /ask endpoint returning Schema.org-typed results. Every NLWeb instance is also an MCP server.

What are the agentic-commerce protocols?

x402: A payment protocol built on the HTTP 402 Payment Required status code, letting agents pay for an API call or resource through a machine-to-machine payment handshake.
Agentic Commerce Protocol (ACP): A protocol for agent-surface checkout — completing a purchase from within an AI agent's interface on the buyer's behalf.
Universal Commerce Protocol (UCP): A protocol focused on merchant interoperability for agent-driven commerce, advertised at /.well-known/ucp.
Agent Payments Protocol (AP2): A protocol for delegated payment authorization, letting a user grant an agent scoped authority to pay on their behalf.

Frequently asked questions

What is agent readability?: Agent readability is how well a website can be discovered, parsed, understood, and acted upon by AI agents — the machine-facing counterpart to human accessibility. It covers discovery files like llms.txt, machine-readable structured data, skill files like AGENTS.md, and protocol manifests for MCP and A2A.
What's the difference between llms.txt and AGENTS.md?: They serve different agents. llms.txt is a curated index that helps retrieval and answer-engine agents find a site's most useful content. AGENTS.md is a skill file that tells coding agents how to build, test, and work inside a codebase. Many projects ship both.
Do I need both an MCP server card and an A2A agent card?: Only if you expose those capabilities. Publish an MCP server card if your site offers tools or resources for AI clients to call; publish an A2A agent card if you run an agent that other agents discover and delegate to. A content site that just wants to be read and cited needs neither.
What is a markdown mirror and why does it matter?: A markdown mirror is a clean-Markdown copy of an HTML page, served at /<page>.md or via content negotiation. It matters because AI extractors parse Markdown far more reliably than HTML cluttered with navigation, scripts, and styling — and several agent-readability checks look for one.
How is agent readability different from SEO?: Traditional SEO optimizes for human click-through from search rankings. Agent readability optimizes for machines that read, cite, and act — emphasizing clean structure, machine-readable manifests, structured data, and content negotiation. They overlap, but agent readability targets answer engines and autonomous agents rather than the blue-link results page.