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Token Saver addresses a critical efficiency bottleneck in LLM workflows by utilizing local hybrid retrieval to drastically reduce token consumption for large document analysis. This approach preserves data privacy and lowers operational costs without requiring complex Python setups, making advanced RAG techniques accessible via standard MCP extensions. By preventing the re-transmission of entire context windows on every query, it offers a practical solution to the compounding expenses associated with processing massive PDFs in chat interfaces.

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

MarkTechPostJuly 30, 20267 min read

Marktechpost AI has released Token Saver, an open-source MCP extension for Claude Desktop that uses local Hybrid RAG to slash PDF token consumption by up to 99% while ensuring absolute document privacy. The post Meet Token Saver: An Open-Source MCP Extension Using Local Hybrid RAG to Cut Claude PDF Token Costs 90-99% appeared first on MarkTechPost.

AI developers, researchers, and professionals frequently hit a frustrating wall when analyzing large documents with LLMs: the hidden, compounding cost of context windows. Pasting a 200-page PDF into a chat isn’t a one-time charge. Because the conversation history is re-sent to the model on every single turn, that massive document is paid for again with every follow-up question. Marktechpost AI team just released Token Saver, an open-source Model Context Protocol (MCP) extension for Claude Desktop (MIT licensed, currently at v1.0). It was developed at Marktechpost AI Media Inc by Arnav Rai (CS student at Rochester Institute of Technology) during his internship at Marktechpost, supervised by Jean-marc Mommessin and Asif Razzaq. Token Saver fundamentally changes how Claude interacts with local documents. By implementing a…

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