Appreciate you opening my eyes to this. Dash is indeed comprehensive, and a much bigger initiative. I wonder how it's handling the documentation staleness issue? New docs are published every minute.

Dash doesn't seem to be open source, plus it's a subscription model. Nothing against that, but OPs solution is MIT licensed.

It didn't get clearer much: so what am I supposed to do to achieve that? If I query AI and it gives me outdated instructions how is your server supposed to help here? I suppose that I am supposed to somehow direct that AI's pipeline to use MCP protocol to make as a next step and query a locally running instance of the server of yours to improve the final answer..? The solution sounds quite ad-hoc: so instead of fixing the problem where it is (in AI's knowledge base), you suggest to apply corrections to it's results by making it query a server each user supposed to run locally? Sounds as a wrong approach to me, I honestly doubt many people would bother working with AI that way, especially given that AI is a paid service.

What I think would be great is either you hosting a central server permanently available to public and somehow convincing major AI service providers to query your servers for solving that narrow scope of tasks, or rather do something similar for open source models available on Hugging Face or something.