Vectorless vs Vector DB: The PageIndex MCP Debate in LLM-Driven Document Retrieval

In the race to fetch the right document with LLMs, PageIndex MCP is pushing a vectorless, human-style approach. It pits a truly human-like index inside the model against traditional Vector DBs. ^[1]

What PageIndex MCP is — PageIndex MCP puts the document index inside the LLM's context window, presenting a hierarchical table-of-contents that the model reasons through, like a reader using a book's index. It’s a move away from vector indexes toward reasoning-based retrieval. ^[1]

How it handles long TOCs and ambiguity • Tree structure lets it handle long TOCs by searching father-level nodes first, then drilling down. ^[1] • For near misses and close titles, every node gets a description or summary to guide decisions. ^[1] • When documents lack a hierarchy, the index becomes a list structure. ^[1]

Where Vector DBs still matter — The team concedes that, in some scenarios like recommendation systems, you need semantic similarity and a Vector DB. ^[1]

Use cases and platform notes — They report the MCP service works well in general financial/legal/textbook/research paper cases. ^[1] Platforms like Claude and Cursor can navigate the index themselves. ^[1]

Closing thought: it’s situational—vectorless depth can shine in structured, reasoning tasks, while embedding-based lookups stay strong for recommendations. ^[1]

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