PageIndex MCP MCP Server
io.github.VectifyAI/pageindex-mcp
Chat with long PDFs via reasoning-based, vectorless RAG over document tree structures instead of embeddings.
What is the PageIndex MCP MCP server?
PageIndex MCP is a Model Context Protocol server that exposes PageIndex's vectorless, reasoning-based RAG system for chatting with long PDFs. It lets MCP-compatible agents like Claude and Cursor navigate documents as hierarchical tree structures and retrieve information through multi-step reasoning rather than vector similarity search, supporting both local and online PDF files.
PageIndex MCP connects LLMs and agents (Claude, Cursor, and other MCP clients) to PageIndex, a vectorless RAG system that represents documents as hierarchical tree structures. Instead of chunking and vector similarity search, it lets LLMs reason over document structure and search the tree like a human navigating a book's index, enabling higher-accuracy, more transparent retrieval from long PDFs without needing a vector database. It supports both local and online PDFs, offers a free tier of 1000 pages, and can be used via a hosted API with simple API key auth, via OAuth for PageIndex Chat users, or run locally via npx for local PDF uploads.
How to install PageIndex MCP
Copy-paste configuration for popular MCP clients.
{
"mcpServers": {
"pageindex-mcp": {
"command": "npx",
"args": [
"-y",
"@pageindex/mcp"
]
}
}
}{
"mcpServers": {
"pageindex-mcp": {
"command": "npx",
"args": [
"-y",
"@pageindex/mcp"
]
}
}
}{
"mcpServers": {
"pageindex-mcp": {
"command": "npx",
"args": [
"-y",
"@pageindex/mcp"
]
}
}
}{
"servers": {
"pageindex-mcp": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@pageindex/mcp"
]
}
}
}claude mcp add pageindex-mcp -- npx -y @pageindex/mcpUse cases
- Chat with long PDFs that exceed normal LLM context limits
- Retrieve information from documents via reasoning-based tree search instead of vector similarity
- Add local or online PDF files to an AI agent workflow for document Q&A
- Integrate document reasoning into custom agent frameworks (Claude Agent SDK, Vercel AI SDK, OpenAI Agents SDK, LangChain)
- Analyze structured long-form documents like reports or manuals without setting up vector DB infrastructure
PageIndex MCP MCP server FAQ
It exposes PageIndex's vectorless, reasoning-based RAG system via MCP so LLMs and agents can chat with long PDFs by navigating a hierarchical tree structure of the document and reasoning through it, instead of relying on chunking and vector similarity search.
It offers a free tier of 1000 pages with unlimited conversations. Beyond that, usage is tied to a PageIndex API key from the PageIndex Dashboard or a PageIndex Chat account.
For PageIndex Chat users, download the .mcpb file from the GitHub Releases page and double-click to install; OAuth authentication is handled automatically. Alternatively, configure it manually as an HTTP MCP server pointing to https://chat.pageindex.ai/mcp or https://api.pageindex.ai/mcp.
Add an MCP server entry pointing to the hosted HTTP endpoint (https://api.pageindex.ai/mcp with a Bearer API key, or https://chat.pageindex.ai/mcp for OAuth users), or run it locally via `npx -y @pageindex/mcp` if you need local PDF upload support (requires Node.js ≥18.0.0).
For developers, a simple API Key from the PageIndex Dashboard (no OAuth flow required) is used with the hosted https://api.pageindex.ai/mcp endpoint. For PageIndex Chat users, OAuth authentication is used and handled automatically via the .mcpb installer or the https://chat.pageindex.ai/mcp endpoint.
Yes, both local and online PDFs are supported. For uploading local PDF files, you can run the local MCP server via `npx -y @pageindex/mcp`, which requires Node.js version 18.0.0 or higher.
README (reference)
Source of truth, from the repository.
PageIndex MCP
If you find this repo useful, please also star our main PageIndex repo ⭐
📘 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 — much like a human would retrieve information using a book's index.
🔌 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 — 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.
✨ Chat to long PDFs the human-like, reasoning-based way ✨
- Support local and online PDFs
- Free 1000 pages
- Unlimited conversations
For more information, visit the PageIndex MCP page.
💡 Looking for a fully hosted experience? Try PageIndex Chat 🤖: a human-like document analyst that lets you chat with long PDFs using the same agentic, reasoning-based workflow as PageIndex MCP.
<p align="center"> <a href="https://pageindex.ai/mcp"> <img src="https://github.com/user-attachments/assets/d807d506-131d-4c7b-837c-96ab1adb2271"> </a> </p>What is PageIndex?
<div align="center"> <a href="https://pageindex.ai/mcp"> <img src="https://docs.pageindex.ai/images/cookbook/vectorless-rag.png" width="70%"> </a> </div>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 — no OAuth flow required.
- Go to PageIndex Dashboard to create an API Key
- Copy the generated key
- Add to your MCP configuration:
{
"mcpServers": {
"pageindex": {
"type": "http",
"url": "https://api.pageindex.ai/mcp",
"headers": {
"Authorization": "Bearer your_api_key"
}
}
}
}
For more details, visit the PageIndex API Dashboard.
For PageIndex Chat Users
If you already have a PageIndex Chat account, you can connect your MCP client directly via OAuth.
Claude Desktop — One-Click Install:
Download the .mcpb file from Releases and double-click to install. OAuth authentication is handled automatically.
Other MCP Clients:
{
"mcpServers": {
"pageindex": {
"type": "http",
"url": "https://chat.pageindex.ai/mcp"
}
}
}
Local MCP Server (with local PDF upload):
If you need to upload local PDF files, you can run the local MCP server (requires Node.js ≥18.0.0):
{
"mcpServers": {
"pageindex": {
"command": "npx",
"args": ["-y", "@pageindex/mcp"]
}
}
}
For more details, visit PageIndex Chat.
Related Links
License
This project is licensed under the terms of the MIT open source license. Please refer to MIT for the full terms.
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