Provenote MCP server

Notebooklab

Teach agents and operators to turn messy long context into structured outcomes they can carry into notes, research threads, drafts, and inspectable results. Quick Result Path · Long Context · Public Proof · Project Status · Docs Second ring: MCP & Integrations · Companion Host Bundles · Distribution · FAQ · Discussions Star Notebooklab if you want a source-heavy AI workbench that stays inspectable after the chat scrollback is gone.

This illustrated overview is a repo-authored summary of the shortest documented path. It is intentionally not presented as a live product recording.

Canonical product path:

messy long context -> structured insight -> note / research thread / draft -> inspectable outcome

That is the first door. MCP, starter bundles, distribution pages, and promotion assets are valuable second-ring surfaces, but they should not outrank the product path.

Agent-facing truth comes first: Notebooklab teaches an agent to read messy context, structure it, move it into note / research-thread / draft lanes, and only then carry that outcome workflow forward through the first-party notebooklab-mcp server. Public skills, host bundles, and registry packs are companion surfaces around that workbench, not the product root.

At A Glance

If you only want the shortest truthful filter before reading deeper, use this table first:

Start Here In 10 Seconds

If you only want the fastest honest map, use this:

Shortened. The full README is on GitHub.