What TokenSaver can do
A structural warm start for AI coding agents in .NET. Hand the model a cheap signature map of your code instead of whole files, so it reads only what it needs. Outlining a file saves 70 – 95 % of tokens versus reading it — the end-to-end win is biggest on smaller, cheaper models and large codebases.
Just talk to your AI in plain text — reference a file by path or ask about a method by name. TokenSaver picks the right tool behind the scenes.
Some non-.NET file types (JS, TS, Python, HTML, CSS, JSON, YAML, XML, C++, X++) also have basic minification support, but those paths are not actively tested and results may vary.
TokenSaver shortens what the AI reads. It doesn't change how code is written. The tool output is a stripped-down view — comments and formatting are gone — so when the AI is about to edit your file, it usually has to re-read the original raw file to match the exact text on disk.
Translation: big savings on understanding and discovery, smaller savings on edit-heavy tasks. That's intentional — accuracy matters more than tokens when code is changing.
When an AI session starts, the model loads the server instructions and all tool descriptions before any tool is invoked — roughly 1,400 tokens of fixed overhead. TokenSaver deducts this cost from the savings reported on the first tool call of each session, so the number you see is always the net saving, not a gross figure that ignores startup cost. Subsequent calls in the same session show full savings with no deduction.
The tools
Percentages compare against reading the whole file — the real alternative when you'd otherwise load it. The end-to-end saving on a task is smaller, and is largest on smaller / cheaper models and large files.
Try saying things like…
Percentages compare against reading the whole file — the real alternative. The end-to-end saving on a task is smaller and depends on the model: largest on smaller / cheaper models, near noise on a top-tier model that already reads tightly.