akexin/mcd-points-actuary
麦麦积分精算师 — 基于麦当劳中国 MCP，把「渠道费 / 券叠加 / 积分机会成本」统一折算到一个目标函数「到手成本 = 现金 + 积分 × 影子价格」，回答同一份订单在到店自取、麦乐送、美团、饿了么哪边到手最便宜、该叠哪几张券、要不要动积分；并给出积分价值矩阵（同积分价值差 13 倍）、抽奖期望值边界与到期止损方案（麦当劳程序员创意开发大赛参赛作品）
Latest metrics (snapshot 2026-10-28)
| stars | 119 |
|---|---|
| forks | 6 |
| issues | 1 |
| pushed_at | 2026-10-10T00:43:16Z |
History
| Date | GitHub stars |
|---|---|
| 2026-10-28 | 119 |
| 2026-10-27 | 119 |
| 2026-10-26 | 121 |
ADC Momentum: 9/100
| Growth velocity | 2 |
|---|---|
| Consistency | 0 |
| Recency | 41 |
| Agent demand | 0 |
| Basis | archive |
ADC Momentum (0-100) = 45% growth velocity (stars/likes gained per day in our daily archive, relative and absolute, percentile-ranked within the category) + 20% consistency (share of recent days with growth) + 20% recency (latest push/update/release, decaying) + 15% agent demand (how often AI agents looked it up via our API/MCP this week). MCP servers use release cadence; LLMs use price cuts, context and recency. Recomputed daily; scores marked "bootstrap" use lifetime rates until the archive has enough days. Method
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[](https://agentdataco.com/repo/akexin/mcd-points-actuary)<a href="https://agentdataco.com/repo/akexin/mcd-points-actuary"><img src="https://agentdataco.com/badge/repo/akexin/mcd-points-actuary.svg" alt="ADC Momentum"></a>Details
| description | 麦麦积分精算师 — 基于麦当劳中国 MCP，把「渠道费 / 券叠加 / 积分机会成本」统一折算到一个目标函数「到手成本 = 现金 + 积分 × 影子价格」，回答同一份订单在到店自取、麦乐送、美团、饿了么哪边到手最便宜、该叠哪几张券、要不要动积分；并给出积分价值矩阵（同积分价值差 13 倍）、抽奖期望值边界与到期止损方案（麦当劳程序员创意开发大赛参赛作品） |
|---|---|
| url | https://github.com/akexin/mcd-points-actuary |
| language | Python |
| license | MIT |
| topics | actuary coupon-optimization loyalty-program mcdonalds mcp model-context-protocol points python |
| created_at | 2026-10-09T07:08:41Z |
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