Meet Token Saver: An Open-Source MCP Extension Using Local Hybrid RAG to Cut Claude PDF Token Costs 90-99%

AI developers, researchers, and professionals frequently encounter a hidden, compounding cost when analyzing large documents with LLMs: the context window. Pasting a 200-page PDF into a chat isn't a one-time charge. Because the conversation history is resent to the model on every turn, that massive document is paid for repeatedly with each follow-up question.

As of mid-2026, token pricing for frontier models like Claude remains a significant operational expense for heavy users, especially those working with large PDFs, technical reports, or academic papers. Traditional approaches either require uploading entire documents (wasting tokens) or complex server-side infrastructure.

Introducing Token Saver

The Marktechpost AI team has released Token Saver, an open-source Model Context Protocol (MCP) extension for Claude Desktop. Licensed under MIT and currently at v1.0, it was developed at Marktechpost AI Media Inc by Arnav Rai (Computer Science student at Rochester Institute of Technology) during his internship, supervised by Jean-marc Mommessin and Asif Razzaq.

How It Works

Token Saver fundamentally changes how Claude interacts with local documents. It implements a Local Hybrid RAG (Retrieval-Augmented Generation) system that runs entirely on your machine. This allows you to ask questions about massive PDFs—whether dense technical manuals, research papers, or financial reports—without ever uploading the file to the model.

Key Results

• Token consumption reduced by 92% to 99%—making it practical to analyze hundreds of pages with dramatically lower API costs
• Privacy is guaranteed—documents never leave your local environment
• Setup is minimal—designed for quick integration with Claude Desktop via the MCP protocol

Why 2026 Context Matters

With token pricing remaining a significant barrier for enterprise-scale LLM usage, tools like Token Saver represent a practical compromise between analysis depth and operational cost. The use of local RAG allows users to maintain control over sensitive data while still leveraging cloud-based reasoning capabilities for queries.

Technical Foundation

Token Saver extracts, chunks, and indexes PDF content locally. When a user asks a question, only the most relevant document segments are sent to Claude's context window—not the entire file. This hybrid approach combines local retrieval with cloud-based reasoning, balancing cost, performance, and privacy.

Conclusion

Token Saver offers a pragmatic solution to one of the most persistent pain points in document analysis with LLMs. By reducing token costs by up to 99% while maintaining privacy, it makes large-scale PDF analysis accessible to individuals and teams without dedicated server infrastructure.

via MarkTechPost