Lucinity is Poor to agents.
Discry independently scored how well an AI agent can discover and understand the Lucinity 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 · 31/100Comprehension
55% of score · 68/100What we found
- An agent can reach a complete, single-page API reference (Redoc-generated, v3.3) covering Actors, Accounts, Transactions, Observations, Cases, Webhooks, Reporting and Segmentation — but discovery is weak: no llms.txt, no llms-full.txt, no .well-known/mcp.json, and no downloadable OpenAPI file at any standard path, so an agent must scrape a 438KB HTML page rather than fetch a machine-readable spec.
- The docs.lucinity.com sitemap lists /docs/* documentation URLs that now 404 — the live reference has moved to the site root — so an agent following the sitemap lands on dead pages, a stale-discovery trap.
- Comprehension is the strong point: endpoint summaries are task-oriented (e.g. 'Creates a new actor entity and assigns it to the specified case'), a numbered Quickstart walks through the core ingest-then-initialize-batch flow, and Redoc gives clean H1→H2→H3 nesting with method/URL shown answer-first.
- Capability boundaries are stated only inline and unevenly — e.g. 'blocking=true is only supported and MUST be set', 'Transaction screening ... may not be configured for your environment', limit 'not greater than 1000' — with no consolidated limitations page and no documented rate limits, so an agent discovers many limits only by failing.
- Access is gated on humans: API keys are obtained by contacting 'your Lucinity Customer Success Team', and there is no self-serve sandbox, no public OpenAPI download, and no MCP-registry presence, so an autonomous agent cannot go from docs to a working call unaided.
What to change
Prioritized by impact on discoverability. You (or your docs platform) deploy these — Discry never touches your API.
- 01Publish the underlying OpenAPI spec at a stable public path (e.g. https://docs.lucinity.com/openapi.json). The Redoc docs are already generated from a v3.3 spec — exposing the raw file is the single highest-impact discovery fix (weight 5) and lets agents load endpoints machine-readably instead of scraping 438KB of HTML.
- 02Add an llms.txt at docs.lucinity.com describing the API (Actors, Accounts, Transactions, Observations, Cases, Webhooks) and linking the Quickstart, Authentication, and OpenAPI spec. This is table-stakes discovery and would give agents a <5K-token entry point into an otherwise monolithic page.
- 03Fix the stale sitemap: the listed /docs/* URLs 404. Regenerate it to point at the live section anchors so agents and crawlers reach real content.
- 04Turn error documentation into recovery guidance: for the common 400/401/404/422/500 responses, state which field or condition triggered it and what to do (e.g. how to correct a 422 'required fields missing', how to re-auth on 401), and document rate limits and any Retry-After behaviour.
- 05Consolidate capability boundaries into one place — supported screening operations, environment-gated features, pagination caps, and rate limits — so an agent can plan around them instead of discovering them through failed calls.
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.
Auth (API key + JWT bearer) and pagination (limit/offset with documented defaults and a 1000-item max) are clearly documented. Error responses use a machine-parseable JSON shape ({"errors": ["string"]}), but per-error recovery guidance is thin. No rate limits and no idempotency-key support are documented.