Backboard scored 100/100 on a DocsAlot curated benchmark pass across 18 public checks. This Documentation benchmark shows where agents can use these docs cleanly and where the biggest points of friction still are.
This report passes 18 of 18 checks, with 0 warnings, 0 failed checks, and 0 skipped checks still shaping the next score lift.
Documentation quality now affects implementation speed, support load, and whether an agent or buyer can stay self-serve. This benchmark shows where the docs already work well and where they still introduce friction.
Good documentation is discoverable, readable in plain text, clear about structure and instructions, and specific enough in examples and next steps that real workflows can keep moving.
These are the clearest signals behind the current score. Start with the strengths that already support discovery and implementation, then use the gaps below to prioritize the fixes most likely to improve AI readability and self-serve onboarding.
For the broader pattern behind these wins, review AI-readable documentation and compare this page with the full docs benchmark leaderboard.
If you want to improve the system behind these gaps, start with API docs automation, the OpenAPI to CLI generator, and this guide to making docs AI-readable.
Open each category to review the checks behind the score and see exactly where agents still hit friction.
Can an agent find the right starting point?
The docs publish llms.txt directly from the docs domain.
The homepage exposes a clear public docs starting point for agents.
Key discovery links resolve cleanly from the public docs surface.
Can an agent fetch a clean text version of the docs?
The docs expose a markdown-friendly representation for agent retrieval.
Core content is available without heavy UI chrome swallowing the copy.
Can an agent read the page without losing important context?
Primary task pages stay within a range that agents can ingest reliably.
Important instructions begin near the top of the page instead of after long nav blocks.
Longer reference content is split cleanly enough that agents can keep context while navigating.
Can an agent understand how the page is organized?
Headings and subsections create a predictable task flow for agents.
Code and examples are separated clearly from surrounding explanation.
Core procedures are written in a way that agents can follow without guessing the next action.
Can an agent trust that URLs will stay predictable?
Core documentation routes are predictable and stable.
The docs do not force agents through noisy redirect chains on key paths.
Can an agent tell whether the docs are fresh and trustworthy?
The docs expose enough freshness cues for agents to trust current guidance.
Readable content stays consistent across public delivery formats.
Freshness and maintenance cues are strong enough for agents to trust the current guidance.
Can an agent access the docs without getting blocked?
Agents can reach the docs without hitting avoidable authentication walls.
Where auth matters, the docs make the boundary clear instead of failing silently.
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Compare related benchmark reports, then move into the guides and tools that improve documentation benchmark scores.
100/100 score in AI. Open the full documentation benchmark report.
97/100 score in AI. Open the full documentation benchmark report.
95/100 score in AI. Open the full documentation benchmark report.
Compare this documentation benchmark report with other public docs benchmark results.
Score your own API docs or developer docs and open the full public report.
See how docs, SDKs, CLIs, and agent-facing outputs can ship from one OpenAPI-driven system.
Use the CLI page when the benchmark gaps are really onboarding and implementation gaps.
Use this guide when you want to improve the same categories this benchmark scores.
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