The agent successfully assembled a comprehensive understanding of Amnesia Reader by combining structured metadata endpoints (agent-manifest, docs-index.json) with prose documentation pages. The site is highly navigable for agents: it publishes machine-readable identity information (what it is, who it's for, pricing, status) in both JSON APIs and markdown, making the task straightforward. The agent identified one notable friction point: the product is pre-alpha, not yet in the Obsidian plugin directory, and the actual installation path is opaque because source repositories are private.

• ›Steps [6] and [15] returned canonical machine-readable APIs (/api/v1/agent-manifest and /docs-index.json) that directly answered pricing ('No commercial product pricing tiers are currently published'), distribution status ('Not currently published in the Obsidian Community Plugin directory'), and product positioning ('pre-alpha Obsidian EPUB/PDF reader'). These were explicitly authored to be consumed by agents and were the primary sources of factual answers.
• ›Steps [12] and [14] returned prose markdown that contextualized the product's value proposition and use case (keeping reading/research in one workspace, source-linked annotations). These complemented the structured data with narrative reasoning and design rationale.
• ›Step [3] provided the 'What is Amnesia?' overview page, which the agent cited to explain core capabilities (EPUB/PDF rendering, source-linked highlights, Obsidian integration). This was discoverable through the site's natural navigation hierarchy.
• ›The site explicitly declares what it does NOT expose: 'The product and documentation repositories are currently private and are not publicly fetchable.' This transparency about source visibility gaps is itself agent-readable and prevented the agent from generating false assumptions about GitHub availability.
• ›The site's agent-readiness is exceptionally high: it publishes a /developers entry point with versioning, API boundaries, and status flags; markdown homepages alongside HTML; structured roadmap and index endpoints; and schema.org markup. The agent did not need to synthesize information from scattered fragments or rely on prior knowledge to answer the core discovery-evaluation questions—the site actively surfaces canonical answers in fetchable form.