◎ Discry Score
launchdarkly.com
devtools · API
A
0 / 100
DISCOVERY0
COMPREHENSION0
Category leader: 96 (A)
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DEVTOOLS · RANK #5 OF 39

LaunchDarkly is Agent-Ready to agents.

Discry independently scored how well an AI agent can discover and understand the LaunchDarkly API from what’s public — not whether it’s usable. Below: every signal we checked, what’s costing the score, and what to change.

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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 · 88/100
OpenAPI specA machine-readable OpenAPI/Swagger spec agents can parse.Pass
llms.txtAn llms.txt index that points agents to the docs that matter.Pass
llms.txt qualityThe llms.txt is focused, current, and well under the size budget.Partial
llms-full.txtA full-text bundle agents can load in one request.Partial
AGENTS.mdAn AGENTS.md that tells coding agents how to build on the API.Pass
.well-known/mcp.jsonA discoverable MCP manifest at a well-known path.Fail
MCP registryThe API is listed in a public MCP registry.Pass
robots.txt AI directivesrobots.txt allows (or explicitly guides) AI crawlers.Pass
SitemapA sitemap so agents can enumerate the docs surface.Pass

Comprehension

55% of score · 96/100
Task-oriented descriptionsEndpoints described by what they accomplish, not just their shape.Pass
Realistic examplesRunnable, real-world request/response examples.Pass
Multi-step workflowsDocs that chain calls into complete jobs an agent can follow.Pass
Error-recovery guidanceDocumented failure modes and how to recover from them.Pass
Answer-first formatThe answer leads; preamble does not bury it.Pass
Capability boundariesClear limits — what the API can and cannot do.Pass
Naming consistencyConsistent, predictable naming across endpoints.Pass
Heading hierarchyClean heading structure agents can navigate.Pass
Markdown docsDocs available as clean markdown, not JS-rendered HTML only.Pass
Token efficiencyDocs are concise enough to fit an agent context window.Partial

What we found

  • An agent can consume every documentation page as clean Markdown simply by appending .md to the URL — and each page's header advertises this, the llms.txt index, and a docs MCP server endpoint. This is best-in-class agent affordance: the docs were clearly built for machine readers, not just humans.
  • LaunchDarkly is one of the most agent-forward devtools scanned: an official hosted+local MCP server (listed on PulseMCP and Glama), an AGENTS.md in the ldcli repo, a public agent-skills/playbooks repo, a GitHub Copilot flag-cleanup agent, and native 'AI Configs' / AgentControl API surfaces.
  • The REST API is fully discoverable and machine-usable: a 2.8 MB OpenAPI 3.0.3 spec is publicly served at app.launchdarkly.com/api/v2/openapi.json covering 385 operations across 243 paths, 100% with summaries and 99% with descriptions.
  • robots.txt allows all crawlers (only an internal /api/fern-docs/ path is disallowed) and the sitemap lists 1,396 doc URLs — an agent searching for LaunchDarkly will find it and is never blocked.
  • The biggest discovery gap is llms.txt sizing: the file is a 191 KB site-wide link dump (well above the ~50 KB agent-friendly target) and llms-full.txt is byte-identical to it rather than containing inlined full content — so an agent pulling the index pays a heavy token cost for capability discovery.

What to change

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

  1. 01Trim llms.txt to an API/capability-focused index under ~50 KB (group by product area with short section descriptions) and reserve the exhaustive link list for llms-full.txt — currently the two files are identical, wasting the convention.
  2. 02Make llms-full.txt actually 'full': inline the core Markdown content (overview, auth, key endpoints, error/rate-limit behavior) rather than duplicating the link index, so an agent can ingest usage patterns in one fetch.
  3. 03Publish a /.well-known/mcp.json pointing at the existing docs MCP server (/docs/_mcp/server) so MCP-aware clients can auto-discover the integration without reading prose — the server already exists; only the discovery manifest is missing.
  4. 04Document idempotency support (idempotency keys for POST/PUT) in the OpenAPI description, or explicitly state that retries are safe via the existing versioning model — agents performing automated writes need this to retry safely after 429/5xx.
  5. 05Add a concise (<5,000 token) 'API overview' / capability-map page (the /docs/api/overview and rest-api intro paths currently 404 as .md) so an agent can grasp core capabilities without parsing the 191 KB index or 50 KB getting-started page.

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.

Personal access tokenService access tokenSession cookie Error format documented Rate limits documented Pagination documented Idempotency documented

Exceptionally complete execution documentation in the OpenAPI spec description: API-key auth via the Authorization header (personal/service tokens, TLS 1.2 minimum), JSON error responses with a documented error-code table, rate limiting with named headers (X-Ratelimit-Global-Remaining, X-Ratelimit-Reset) plus Retry-After and explicit jitter/backoff guidance, HATEOAS pagination via _links (self/next/first/last) with summary-vs-detailed representations, and dated API versioning with clearly marked beta resources. Idempotency keys are not documented.

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