Show HN: A Vectorless LLM-Native Document Index Method

The word "index" originally came from how humans retrieve info: book indexes and tables of contents that guide us to the right place in documents.

Computers later borrowed the term for data structures: e.g., B-trees, hash tables, and more recently, vector indexes. They are highly efficient for machines; but abstract and unnatural: not something a human, or an LLM, can understand and directly use as a reasoning aid. This creates a gap between how indexes work for computers and how they should work for models that reason like humans.

PageIndex is a new step that "looks back to move forward". It revives the original, human-oriented idea of an index and adapts it for LLMs. Now the index itself (PageIndex) lives inside the LLM's context window: the model sees a hierarchical table-of-contents tree and reasons its way down to the right span, much like a person would retrieve information using a book's index.

PageIndex MCP shows how this works in practice: it runs as a MCP server, exposing a document's structure directly to LLMs/Agents. This means platforms like Claude, Cursor, or any MCP-enabled agent or LLM can navigate the index themselves and reason their way through documents, not with vectors/chunking, but in a human-like, reasoning-based way.