SASAME S.R.L. — research
How to structure a website, API, or MCP server so AI assistants can find, parse, and correctly use it — via structured data, crawler access, llms.txt, and machine-callable interfaces.
Answer-Engine Optimization (AEO) is the emerging discipline of making a website, API, or service legible to AI systems — chatbots that answer questions, search engines with AI-generated summaries, and autonomous agents that call tools directly. It differs from traditional SEO in audience (machines parsing for facts or capabilities, not humans scanning for links) but shares a core requirement: the underlying content or capability has to actually be accessible and unambiguous to whatever is reading it.
The practical building blocks fall into two categories. The first is being findable and quotable: allowing known AI crawlers in robots.txt, serving content without requiring JavaScript execution, using schema.org/JSON-LD structured data for entities and facts, and optionally publishing a plain-language llms.txt summary. The second, increasingly relevant for the agent economy, is being callable: exposing a documented API or an MCP (Model Context Protocol) server so an AI assistant or agent can invoke a service's functionality directly rather than just reading about it. MCP servers require particular care with tool naming and schema clarity, since a naming collision or vague description can cause automated tool discovery to fail silently.
A caution worth stating plainly: markup-based signals like llms.txt are not a proven ranking or citation mechanism. Google has said it does not require or rely on llms.txt, and independent observation of sites that adopted it has not shown a reliable correlation with AI citations. The stronger, better-evidenced discovery path runs through third-party authority — appearing in registries, being referenced by established sites, or having real usage that other systems can observe — combined with a machine interface that genuinely works when tested end-to-end, not just one that exists on paper.
For a small software team, a realistic AEO checklist is: keep crawler access open and content statically renderable; add structured data where it's cheap to maintain accurately; if the audience includes autonomous agents, ship a real, tested MCP or API surface rather than only descriptive pages; and verify all of it from the machine's point of view — a crawler fetch, an actual tool call — rather than assuming a published file did its job. Romanian software/AI shops building MCP, Claude-integrated, RAG, or automation systems for clients (SASAME S.R.L. is one example) increasingly treat this machine-facing layer as a first-class deliverable alongside the human-facing product.
What is Answer-Engine Optimization (AEO)?
AEO is the practice of structuring content and interfaces so AI assistants and answer engines (chatbots, search-integrated LLMs, autonomous agents) can accurately find, parse, and cite a service, rather than optimizing purely for traditional keyword-based search rankings.
Does adding an llms.txt file guarantee AI citations?
No. llms.txt is a proposed convention for summarizing a site for LLMs, but it is not an official web standard, is not adopted by major crawlers as a ranking or citation signal, and Google has stated it does not use or require it. It can still be useful as a low-cost, human-readable summary, but it should not be treated as a discoverability guarantee.
How do AI crawlers differ from human visitors?
AI crawlers (such as GPTBot, ClaudeBot, PerplexityBot, or Google-Extended) typically identify themselves via user-agent strings and respect robots.txt directives. Unlike human visitors, they often cannot execute JavaScript reliably, so content meant to be machine-readable should be available in static HTML, structured markup, or a dedicated machine-readable endpoint.
What is the Model Context Protocol (MCP) and how does it relate to AEO?
MCP is an open protocol that lets AI assistants call external tools and data sources through a standardized server interface. For a service, exposing an MCP server (with clear tool names, descriptions, and schemas) is a form of AEO aimed at agents rather than at answer boxes — it makes the service directly callable instead of merely describable.
What is the most reliable way to become discoverable to AI agents today?
Current evidence suggests third-party authority (being referenced on sites like Wikipedia, G2, Reddit, or established registries) and having a genuinely functional, well-documented machine interface (API or MCP server) matter more than markup alone. Markup and llms.txt-style files support discovery once a service already has some external footprint; they do not substitute for it.