AT LEAST

Saving the World from AI... with AI

AI coding assistants have a token problem. When you ask an assistant to find a function or trace a dependency, it tends to read whole files, often hundreds of them, burning input tokens that map directly to GPU-seconds of inference energy. The energy isn't theoretical. It's measured in megawatt-hours when you aggregate across enough developers.

jCodeMunch-MCP fixes that at the source. It indexes a codebase, then serves AI agents the exact function, class, or symbol they asked for instead of the whole file. The agent gets the same answers from a fraction of the tokens. The energy that would have been spent processing the rest of the file never gets spent.

That reduction is measurable against SCI for AI, the AI carbon standard ratified by the Green Software Foundation in December 2025 (the one Microsoft, Google, UBS, and Accenture built). The full case study, with the math, the A/B tests, and the methodology, is published at our wiki. Per-task token reduction lands in the 15 to 25 percent range end-to-end and up to 99 percent against full-file-read baselines. The carbon savings scale linearly from there.

Real Elimination, Not Paperwork

jCodeMunch-MCP doesn't buy offsets, renewable energy credits, or power-purchase agreements. SCI for AI explicitly rejects all of those as score-reduction mechanisms. The only thing the standard credits is causing fewer GPU-seconds to be consumed in the first place. That's what's happening here.

The Math Is Public. Anyone Can Verify It.

We are not asking you to take our word for it. Every claim on this page traces back to public artifacts.

• Live counter endpoint. Hit https://j.gravelle.us/APIs/savings/total.php in your browser. It returns JSON. The number above is what came back when this page loaded, refreshed every 30 seconds.
• SCI for AI methodology. Token reduction translates to inference-energy reduction at the Consumer boundary, per the spec ratified by GSF in December 2025. The full case study includes the formula, the per-token energy bounds, and the A/B test data.
• No offsets, no RECs, no PPAs. SCI for AI rejects all financial-instrument-based reductions. Only elimination counts. This is elimination: tokens that would have been processed on a GPU, that weren't.
• Reproducible per-task savings. Any developer can log usage.input_tokens from the LLM provider's API, run a task with and without jCodeMunch-MCP, and see the reduction directly.

Cut your token costs and your carbon footprint.

One license, indefinite use. No subscriptions. The same tool measured in the case study above.