pageindex-mcp

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PageIndex MCP

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📘 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 App 🤖: 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

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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.

1. Go to PageIndex Dashboard to create an API Key
2. Copy the generated key
3. 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 App Users

If you already have a PageIndex App 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://app.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 App.

Related Links

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License

This project is licensed under the terms of the MIT open source license. Please refer to MIT for the full terms.