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 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 Local Hybrid RAG (system directly on your machine, it allows you to ask questions about massive PDFs without ever uploading the actual file to the model. Token consumption is slashed by 92% to 99%, privacy is guaranteed, and setup requires exactly zero Python environments or terminal configurations. How it looks! The Hidden Token Drain of Large PDFs Most users assume that when they drop a PDF into Claude, it simply extracts the text. In reality, Claude’s default behavior converts each page into an image to preserve charts and layouts, while separately extracting text. Before a single image token is even counted, the text alone can run 1,500 to 3,000 tokens per page. While Prompt Caching and Claude Projects help soften this blow, they don’t solve the core issue: the entire document still crosses the boundary to the provider’s servers. Furthermore, if you only need two relevant paragraphs from a 1,000-page textbook, forcing the LLM to search the entire 1,000 pages itself is both inefficient and prone to hallucination. How Local Hybrid RAG Solves the Problem Marktechpost’s Token Saver acts as a local MCP server : a lightweight background program on your machine that Claude can call as a tool. The PDF never leaves your hard drive. When you ask a question, Token Saver utilizes Local Hybrid RAG to find the answer. Hybrid RAG is the gold standard for document retrieval because it combines two powerful search methods: Keyword Matching (BM25): Runs over SQLite’s built-in FTS5 search (weighted at 0.4) to find exact terminology. Semantic Search (Cosine Similarity): Uses a local all-MiniLM-L6-v2 embedding model (weighted at 0.6) to understand the meaning of your question, rather than just matching exact words. By blending these two approaches locally, the server searches the file and hands Claude only the highly relevant passages. Claude then synthesizes the answer, citing the exact page numbers and providing a running tally of the tokens you just saved. The 8-Stage Processing Pipeline Token Saver runs entirely inside a single, long-running local process. Before any text reaches Claude, the local Hybrid RAG pipeline executes eight rapid steps: Extract: Uses pypdfium2 (with pypdf as a fallback) to pull text. Chunk: Splits text into 180-word passages, overlapping by 40 words so context is never blindly cut. Score (Hybrid RAG): Evaluates chunks using the blended BM25 and local embedding model. Gate: Enforces a quality threshold. Passages sharing no exact keywords must pass a semantic similarity floor of 0.25 to qualify. Deduplicate: Drops near-identical passages. Trim: Narrows the passage down strictly to the sentences that answer the user’s prompt. Budget: Caps the total payload sent to Claude at 8,000 characters. Envelope: Wraps the retrieved text in a document-chunk element containing the source file and precise page number. (Note: The embedding model is optional but recommended. If it fails to load, the system gracefully falls back to keyword-only matching). The Numbers: 99% Savings at Scale Bec