The agent assembled a comprehensive explanation of DeepL's API offering by directly fetching developer documentation pages, supplemented with external comparison articles to answer the alternatives question. The site's developer docs are well-structured and machine-readable via llms.txt index, though pricing information required a redirect to the main DeepL site (deepl.com/pro-api), creating a friction point that the agent worked around by consulting external sources.

• ›Steps [4], [5], [8], [11], [12], [13] directly returned the core content: what DeepL does (About page), how to get started (Quickstart with pricing link), supported SDKs, supported languages, usage limits, and glossary features. The documentation is well-authored and complete for technical understanding.
• ›The site does NOT publish pricing information on developers.deepl.com itself—step [2] returned 404 for /pricing. The agent had to rely on external web search results (steps [7], [10]) and external comparison articles to answer 'how it's priced' and 'how it's different from alternatives.' This is a significant agent-readiness gap: pricing is essential to the discovery-evaluation task but is not served by the developer domain.
• ›The llms.txt artifact (step [3]) successfully surfaced the full documentation index, enabling efficient discovery of relevant pages. However, the pricing page redirect and lack of competitor comparison within the developer docs forced the agent to supplement its answer with external sources rather than relying solely on primary site content.
• ›Language support and feature availability inconsistencies (e.g., glossaries only work for 11-15 languages) were documented but not prominently highlighted in early sections, requiring the agent to find and synthesize details from the supported-languages page to give a complete picture.