Emblem AI is Not Agent-Ready to agents.

What we found

• —emblem.ai is effectively a parked/placeholder domain, not a live API product. The homepage and its only sitemap entry (/lander) both return empty content across four scraping engines, so an agent arriving here finds no capabilities to act on.
• —The domain is crawler-friendly on paper — robots.txt allows all agents and advertises an LLM-Policy and sitemap — but that openness leads nowhere: the sitemap lists a single non-documentation lander URL.
• —The file served at /llms.txt is not an llms.txt at all; it is a training-opt-out policy stub ('Disallow-Training: /') with no title, description, or links to any API documentation, so it gives an agent zero usable context.
• —No OpenAPI/Swagger spec, no /.well-known/mcp.json, no llms-full.txt, no AGENTS.md, and no MCP registry listing exist. There is no discoverable machine-readable surface of any kind.
• —Comprehension is unscorable and scores 0 because no documentation pages exist to evaluate — an agent cannot learn what Emblem AI does, let alone how to call it.

What to change

Prioritized by impact on discoverability. You (or your docs platform) deploy these — Discry never touches your API.

1. 01Publish actual API documentation. Before any agent-readiness work matters, emblem.ai needs a real product surface: an overview page describing what the API does and at least a few endpoint reference pages.
2. 02Replace the /llms.txt policy stub with a genuine llms.txt: a titled, described index that links to the API overview, authentication, and key endpoint docs so agents can orient in one fetch.
3. 03Expand sitemap.xml beyond the single /lander URL to include documentation pages once they exist, so crawlers and agents can discover the real content.
4. 04Publish a public OpenAPI/Swagger spec at a stable path (e.g. /openapi.json) — the single highest-weighted discovery signal and the fastest way for an agent to understand the full API surface.
5. 05Once docs exist, add task-oriented endpoint descriptions, realistic multi-language code examples, and end-to-end workflow guides so an agent can chain operations rather than reading endpoints in isolation.