Reasoning-based RAG system for chatting with long PDFs. Supports local and online files.
PageIndex MCP Server
PageIndex MCP (io.github.VectifyAI/pageindex-mcp) provides a reasoning-based RAG system for chatting with long PDFs. It supports working with both local and online files. The server is associated with PageIndex and is documented via a PageIndex MCP home page.
π οΈ Key Features
Reasoning-based RAG for long PDF Q&A/chat
Input support for local files
Input support for online files
π Use Cases
Chatting with long PDF documents using retrieval-augmented generation
Handling documents available either locally or via online sources
β‘ Developer Benefits
RAG workflow intended for PDF content
Consistent interface for local and online file inputs
β οΈ Limitations
Available metadata does not specify tool names, toolCount, configuration options, or supported document formats beyond βlong PDFs.β
π PageIndex is a vectorless, reasoning-based RAG system that represents documents as hierarchical tree structures. It enables LLMs to navigate and retrieve information through structure and reasoning, not vector similarity β much like a human would retrieve information using a book's index.
π PageIndex MCP exposes this LLM-native, in-context tree index directly to LLMs via MCP, allowing platforms like Claude, Cursor, and other MCP-compatible agents or LLMs to reason over document structure and retrieve the right information β without vector databases.
Want to chat with long PDFs but hit context limit reached errors? Add your file to PageIndex to seamlessly chat with long PDFs on any agent/LLM platforms.
β¨ Chat to long PDFs the human-like, reasoning-based way β¨
π‘ Looking for a fully hosted experience? Try PageIndex App π€: a human-like document analyst that lets you chat with long PDFs using the same agentic, reasoning-based workflow as PageIndex MCP.
PageIndex is a vectorless, reasoning-based RAG system that generates hierarchical tree structures of documents and uses multi-step reasoning and tree search to retrieve information like a human expert would. It has the following key properties:
Higher Accuracy: Relevance beyond similarity
Better Transparency: Clear reasoning trajectory with traceable search paths
Like A Human: Retrieve information like a human expert navigates documents
No Vector DB: No extra infrastructure overhead
No Chunking: Preserve full document context and structure
No Top-K: Retrieve all relevant passages automatically
PageIndex MCP Setup
For Developers
Connect PageIndex to your agent framework or AI SDK via MCP. Works with Claude Agent SDK, Vercel AI SDK, OpenAI Agents SDK, LangChain, and any MCP-compatible client. Simple API Key authentication β no OAuth flow required.