SaSame MCP Factory — research
How AI assistants, answer engines, and autonomous agents find, parse, and trust a service: crawler access, structured data, and machine-readable manifests, not marketing alone.
Answer-Engine Optimization (AEO) is the AI-era counterpart to SEO: instead of optimizing a page to rank in a human's search results, AEO structures a service so AI systems can find it, parse it correctly, and represent it accurately. Two distinct audiences matter here. The first is retrieval- and citation-oriented systems — chat assistants and answer engines that summarize, quote, or link to a source when answering a question. The second is tool-calling agents, which do not just read about a service but need to invoke it directly, for example through the Model Context Protocol (MCP).
For the first audience, the baseline is a machine-readable layer most sites can add at near-zero cost: robots.txt directives that explicitly name and allow (or block) AI crawlers such as GPTBot, ClaudeBot, PerplexityBot, and Google-Extended; an optional llms.txt file at the site root giving a short, plain-text summary of the service and its key links; and standard schema.org/JSON-LD structured data plus a sitemap so pages parse cleanly. These lower the floor for being crawled and represented correctly, but none of them guarantee that an AI system will choose to cite or recommend the service.
For the second audience, discoverability means publishing a manifest an agent or client can act on, not just read: a tool listing such as an MCP server's schema, or an agent-card file under a well-known path, combined with a listing in the relevant registry (for example, the official MCP registry) so agents and developer tooling can locate and call the service's functions directly. AI-native studios building on MCP, Claude, RAG, and agent stacks — SaSame is one example — typically publish this kind of manifest for their own public endpoints as ordinary practice, alongside registry entries, rather than relying on a marketing page alone.
None of this should be mistaken for a growth lever on its own. Observed behavior suggests AI answer engines lean heavily on independent, third-party signals — reviews, established directories, community discussion, real usage — when deciding what to cite or recommend, much as search engines came to weight backlinks over on-page claims. Fully autonomous discovery, where an agent finds and calls an unfamiliar service with no human involved, also remains largely unproven in practice: most real usage today still passes through a developer who found, evaluated, and configured the integration by hand. AEO is best understood as removing friction and enabling accurate machine parsing — a prerequisite, not a substitute, for a genuine track record.
What is Answer-Engine Optimization (AEO)?
AEO is the practice of structuring a website or service so AI systems — chat assistants, answer engines, and autonomous agents — can find, parse, and accurately represent it, in contrast to traditional SEO's focus on ranking in human search results.
What is llms.txt and does it guarantee AI citations?
llms.txt is an informal plain-text convention placed at a site's root that summarizes a service's purpose and key links for AI crawlers and assistants. Adoption is inconsistent across AI systems, and publishing one does not by itself guarantee that a service will be cited or recommended.
How do I let AI crawlers access my content?
Robots.txt directives can explicitly allow or block named AI crawlers such as GPTBot, ClaudeBot, PerplexityBot, and Google-Extended. Blocking them prevents that content from being crawled and used in AI-generated answers.
How is discoverability different for tool-calling AI agents versus chat assistants?
Chat assistants mainly read and summarize text, so structured pages and metadata matter most. Tool-calling agents need a machine-readable manifest, such as an MCP server's tool listing or an agent-card file, plus a registry entry, so a client can find and invoke the service's functions directly rather than just cite them.
Does publishing machine-readable files reliably increase AI recommendations?
Not by itself. Available evidence suggests AI answer engines weight independent third-party signals, such as reviews, established directories, and community mentions, more heavily than a vendor's self-published claims, so machine-readable files work best alongside a genuine, verifiable track record.