[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f1dfzbubrz9udd":3},{"code":4,"message":5,"data":6,"timestamp":26},10000,"success",{"full_name":7,"position":8,"description":9,"language":10,"stars":11,"forks":12,"stars_gained":13,"age_days":14,"hn_threads":15,"one_liner":16,"summary_md":17,"topic":18,"github_topics":21,"license":22,"homepage":23,"updated_at":24,"history":25},"yeqv111\u002Fmccarbon",22,"麦麦碳账本 · 基于麦当劳 MCP 的个人餐饮碳足迹估算与减排模拟器 | Personal dining carbon footprint estimator powered by McDonald's MCP","Python",1382,37,112,12,[],"Estimates one person's yearly McDonald's carbon footprint from order history and simulates how swaps would reduce it.","## The problem\n\nYou have a rough sense that beef is worse for the climate than chicken, but no way to turn that into a personal number: how much CO2e did your own year of McDonald's orders actually produce, and which specific habit contributes most? mccarbon pulls your order history through McDonald's official MCP and turns it into a per-person carbon ledger with a grade, a monthly curve, and a breakdown across food, packaging and delivery.\n\n## What's different\n\nThe usual options are generic carbon calculators, which use national average diets and never touch your receipts, or nothing at all. mccarbon's distinguishing choice is methodological transparency: it splits emissions into three layers with separate factors, uses published measured values where they exist (UK McDonald's items via Vegan Society, ADEME Agribalyse, EU beef LCA) and falls back to per-category medians elsewhere, and says so in the README. On top of that sits a reduction simulator that quantifies specific swaps — red meat to white, delivery to pickup, dropping combo bundles — in kilograms and percent of total. The output is a single self-contained HTML page with inline SVG charts, no CDN or JS, so it is screenshot-friendly and shareable.\n\n## Who it's for\n\nIndividual McDonald's customers in China who have an MCP token and a few months of orders and are curious about the environmental shape of their own eating. It also suits people who want to read and audit the assumptions, since every factor lives in one Python file. Look elsewhere if you want absolute, defensible carbon accounting for reporting purposes — the README itself calls the numbers estimates — or if you don't order through channels the MCP covers. Vegans and people who rarely eat fast food will find the simulator has little to optimize.\n\n## Cost of adoption\n\nPython 3.9+, zero third-party dependencies, MIT license, and an offline `--mock` mode with a full year of sample data, so you can see the output before obtaining any credentials. Real data requires a McDonald's MCP token, which you must obtain yourself and which can expire or fail with 401. The factor table covers a Chinese menu mostly by extrapolation rather than measurement, so precision is limited by category medians. The project is 12 days old and its README is the main documentation; there is a small test suite and no evidence of long-term maintenance yet.\n\n## Verdict\n\nInterest is real but very new: about 1,566 stars in under two weeks and roughly +124 stars per day, placing it on a new-project board rather than the main trending board, with no Hacker News discussion. It was built as a contest entry — the README mentions a competition declaration — which explains the heavy methodology section and the competitor analysis document; whether it continues past the contest is unknown. As a personal curiosity tool with a genuinely readable accounting model, it is cheap to try: one command and no installation. As infrastructure or as a source of authoritative emissions figures, it is not there yet.",{"slug":19,"name":20},"productivity","Productivity & Utilities",[],"MIT",null,"2026-10-25T02:36:37Z",[],1792918360]