PageIndex
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
PageIndex is an MCP server that provides vectorless, reasoning-based document retrieval for AI agents working with long, complex documents. Instead of using traditional vector databases and chunking, it generates hierarchical tree-structure indexes from PDFs (similar to a dynamic table of contents) and enables LLMs to reason through these structures for context-aware retrieval. The server is particularly effective for professional documents like financial reports, legal filings, technical manuals, and academic papers, achieving 98.7% accuracy on the FinanceBench financial document QA benchmark. Developers can self-host the open-source version or integrate the cloud service via MCP, API, or a dedicated chat platform.
Key Features
Use Cases
- 01Analyzing financial reports and SEC filings with precise context extraction
- 02Navigating legal documents and regulatory compliance materials for relevant clauses
- 03Extracting information from technical manuals and engineering documentation
- 04Querying academic research papers and medical literature with domain awareness
- 05Building conversational AI assistants that understand document structure and hierarchy
- 06Creating explainable document QA systems where every answer traces back to specific sections
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PageIndex — FAQ
What is the PageIndex MCP server?+
PageIndex is an MCP server that enables AI agents to perform reasoning-based document retrieval by creating hierarchical tree indexes from long documents. Unlike traditional vector RAG, it uses LLM reasoning over document structure rather than semantic similarity search.
How do I install the PageIndex MCP server?+
Install Python dependencies with pip, set your OpenAI API key (or other LLM provider key via LiteLLM) in a .env file, then configure your MCP client to run the PageIndex server. The self-hosted version uses standard PDF parsing; the cloud service offers enhanced OCR.
Which AI clients work with PageIndex?+
PageIndex works with any MCP-compatible client including Claude Desktop and other applications supporting the Model Context Protocol. It can also be integrated via API or used through the PageIndex Chat platform.
Do I need API keys to use PageIndex?+
Yes, you need an LLM API key (OpenAI by default, or any provider supported by LiteLLM) for generating tree structures and performing reasoning-based retrieval. Set the key in a .env file as OPENAI_API_KEY or the appropriate provider key.
Is PageIndex free to use?+
The open-source self-hosted version is free to use with standard PDF parsing. The cloud service with enhanced OCR and tree building is available via paid plans, with enterprise deployment options also offered.
What makes PageIndex different from vector-based RAG?+
PageIndex performs retrieval through LLM reasoning over hierarchical document structure rather than vector similarity matching. This provides context-aware, traceable, and explainable results without requiring vector databases or document chunking.
How do I install PageIndex?+
Open the source repository on GitHub and follow its README. PageIndex is a mcp server — MCP Agents Market links you directly to the official repo.
Is PageIndex free?+
PageIndex is an open-source project hosted on GitHub. Check the repository for its license and any usage requirements.