DocsAlot

The MCP + llms.txt combo makes sense for the AI-answers use case. One thing I'd want to know before pointing this at our repo: since it's reading source code to detect stale docs, how does it handle internal-only context, things like code comments referencing unreleased features or internal tooling that shouldn't end up summarized into a public-facing llms.txt. Is there a way to mark certain source paths as off-limits for the AI-facing output, or is that on the roadmap?

The "agents stop reading stale context" line is the real problem statement here — most doc tools optimize for human search, but agent context freshness is a different failure mode entirely. How are you handling versioning when the underlying docs change — does the MCP endpoint serve live content, or is there a sync/caching layer in between?

Congrats on your launch! 🎉

how does docsabot actually keep things in sync when a source article changes, is it just polling or is there some kind of webhook setup

AWESOME!

The failure mode I keep hitting wiring agent docs through MCP: the agent grabs a confident, plausible snippet that's one version stale and never flags it, because retrieval has no notion of 'this section is old.' Emitting llms.txt is the easy part. The valuable bit is a freshness signal inside the MCP response itself, so an agent can tell a current param from a deprecated one. Does your outdated-doc detection surface a staleness marker in the MCP payload, or only in the dashboard?

I like the focus on keeping human facing docs and agent readable documentation tied to the same sourcehow do you handle versioning and change tracking when product docs or API references are updated frequently?

Connected my Notion help center and the hosted MCP endpoint worked first try, which never happens for me. The llms.txt output was surprisingly clean compared to what I had hacked together before.

Finally, someone is tackling this! Writing docs that LLMs can easily parse while keeping them readable for human developers is such a tricky balance right now. Does this integrate directly with GitHub repos to keep the docs synced with the codebase?

"Your docs show up in AI answers" depends entirely on how the underlying models are trained and updated, which DocsAlot doesn't control. The llms.txt standard is still not universally respected across major models and crawlers. What's the realistic expectation for how quickly and consistently docs actually surface in AI answers after setting this up, or is that more of a long-term bet on the standard gaining broader adoption?

This feels timely. Docs are no longer just for users and support teams. AI agents also need a reliable source of truth now.Curious how DocsAlot handles drift over time. If the product changes, does it detect outdated docs automatically, or do teams still need to manually trigger updates?

the "detect outdated docs from source-code" part is the piece i'd want to stress test before trusting it on a real repo. false negatives are one thing, but false positives (flagging a doc as stale when the underlying behavior didn't actually change) seem like the bigger risk since that's what erodes trust in the tool and gets people ignoring the suggestions after a few bad flags. how does it decide a doc is stale, does it diff behavior or just correlate with commit/PR activity touching related files?

qq does this support openapi/swagger imports or is it text only for now? congrats for shipping today @haya_jawed

Faizan, this lands at the right time :) But my honest first thought is the one you'll probably hear a lot: llms.txt, skill.md and hosted MCP are becoming checkboxes, Mintlify and GitBook are already bolting them on. Emitting an AI-readable format won't stay a differentiator for long.The line that actually caught my eye is the one you dropped to Andras: "data on how agents traverse help-centers." That feels like the real moat, using how agents actually read docs to restructure the content for them, not just expose it in a format everyone will have in 6 months. Is that where you're heading (scoring and reshaping docs for agent comprehension), or is the core bet still the unified output layer? Congrats on the launch! ;)

This feels very relevant right now.Docs used to be "just" onboarding and support, but now they also decide what AI tools and agents understand about your product. If the docs are stale, the agent context is stale too.We’re working on our own help center and llms.txt setup for our product, so I really like the idea of treating documentation as a maintained knowledge layer, not a side project someone updates when they remember :)Curious how DocsAlot handles product changes over time. does it detect when docs are outdated from changelogs/GitHub/product updates, or is the maintenance workflow more manual right now?