Agent satisfied the task by assembling a coherent explanation of Mira from the site's homepage, docs, and GitHub repo. The site was navigable but sparse—core positioning was clear, but comparative details and business/maintenance context required external (GitHub) sources and model inference.

• ›Homepage clearly stated 'open source static site framework' and key features (1 KB runtime, morphing animations, AI-agent content) in the first fetch, but pricing required no explicit site statement—the agent correctly inferred 'free' from MIT/Apache-2.0 license mentions without needing to search.
• ›GitHub repo [step 6] was the only source that explicitly described differentiators (View Transitions, 5–7 KB pages, MCP integration); the main site mentioned these features but didn't explain why they mattered vs. Hugo/Astro.
• ›Site structure was well-formed (docs/, blog/, GitHub links all present and reachable from homepage [step 3]), but the blog was empty and docs were sparse—agent had to rely on GitHub README and model memory to explain why developers should choose it.
• ›The agent correctly noted missing information (no explicit comparisons, empty blog, team/roadmap opacity) and did NOT fill those gaps with unsupported claims, showing appropriate confidence calibration.
• ›Web search [steps 7–9] returned results for unrelated 'Mira' products (whiteboard tool, CMMS, portfolio templates), forcing the agent to disambiguate and confirm the framework was free—site provided no meta tags, structured data, or llms.txt to prevent this confusion.