AI developers, researchers, and professionals often run into a hidden cost when they load large PDFs into language models. Every time they ask a follow‑up question the entire document is resent to the model, so token usage multiplies with each turn. Even a single 200‑page file can cost thousands of tokens per query, making repeated analysis expensive and slow.
An open‑source Model Context Protocol extension solves this by running a local hybrid retrieval system on the user’s machine. The PDF never leaves the hard drive. When a question is asked, the tool searches the document locally using a combination of keyword matching and semantic search, then returns only the most relevant passages to the model. This keeps the token payload tiny—often under one percent of the original document size—while still giving the model enough context to generate accurate answers.
Because the processing happens locally, privacy is guaranteed. No data is uploaded to external servers, and the tool works with a simple folder allowlist so users control exactly which files can be accessed. Setup requires no Python environment or terminal commands; the extension is installed as a single bundle and configured by pointing it to a chosen folder.
Benchmark tests show token savings ranging from 92% to over 99% for documents ranging from dozens to hundreds of pages. Users can verify answers by checking the cited page numbers in their local PDF, and the tool gracefully falls back to keyword‑only search if the embedding model fails to load.
For anyone repeatedly working with large technical reports, legal texts, or research papers, this approach removes the repetitive token tax, cuts costs, and keeps data private.
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