vitrine3d is Good to agents.
Discry independently scored how well an AI agent can discover and understand the vitrine3d API from what’s public — not whether it’s usable. Below: every signal we checked, what’s costing the score, and what to change.
SCORED UNDER RUBRIC 1.2 · A full re-launch under Discry Score 2.5 — a new behavioral instrument, not comparable to these scores — is in progress.
Discovery
45% of score · 57/100Comprehension
55% of score · 93/100What we found
- An agent can operate this API end-to-end from documentation alone: the API page is explicitly framed for 'your own scripts, CI pipelines, or AI agents,' and a full Quick Start chains list → upload GLB → PATCH scene config → publish → fetch embed with realistic curl examples.
- vitrine3d ships a first-class MCP server (documented at /docs/mcp with named tools like vitrine_set_config and read-only resources) and is listed in the Glama MCP registry — an agent discovering it via MCP tooling would find it immediately.
- Comprehension is near-perfect (93/A): task-oriented endpoint descriptions, consistent snake_case config fields, documented rate-limit tiers, file-size/format boundaries, a dedicated Troubleshooting page, and a clean llms-full.txt markdown dump.
- Discovery is the weak dimension (57/D): there is no publicly accessible OpenAPI/Swagger spec (openapi.json 404s on both the docs and api hosts), which is the single biggest scored gap for a REST API an agent would want to introspect programmatically.
- No AGENTS.md and no .well-known/mcp.json — an agent relying on those specific discovery conventions gets nothing, even though the underlying MCP server and docs are excellent.
What to change
Prioritized by impact on discoverability. You (or your docs platform) deploy these — Discry never touches your API.
- 01Publish a machine-readable OpenAPI 3.x spec at a stable URL (e.g. https://api.vitrine3d.com/openapi.json) and link it from the API docs page — this is the highest-leverage discovery fix (+5 weight) and lets agents auto-generate clients.
- 02Add a .well-known/mcp.json at vitrine3d.com declaring the existing MCP server, its tool surface, and auth modes, so agents using well-known discovery find the server without reading prose docs.
- 03Add an AGENTS.md to the public repo (or publish one) summarizing the API base URL, auth, and the upload→configure→publish→embed workflow for coding agents.
- 04Provide code examples in at least one additional language beyond curl (JavaScript/Python) on the Quick Start and endpoint pages to move realistic-examples from partial to full.
- 05Expose an llms-full.txt at the root domain (vitrine3d.com/llms-full.txt currently serves the homepage fallback) so agents that probe the apex domain reach the same comprehensive dump already available at docs.vitrine3d.com.
Execution coverage · INFORMATIONAL, UNSCORED
Whether an agent can actually complete a call and recover from errors is the deeper Audit layer — documented here, but not part of the Discry Score.
Execution characteristics are documented unusually well for a scanned API: three auth modes (API key, Supabase JWT, anonymous), a machine-parseable JSON error envelope with `error`/`message` fields and an HTTP status table, tiered rate limits (20/60/300 req/min) surfaced via `X-RateLimit-Remaining` and `Retry-After` headers. Pagination and idempotency are not documented. Claims are documentation-only and unverified without a live audit.