GreenPick

Name: GreenPick
Author: bithug01

作者：bithug01版本 0.6.2i★ –i在你的电脑上运行

注册名称io.github.bithug01/greenpick

安装

概览

作者提供的说明（英文原文）

Greener, cheaper cloud infra decisions — carbon + cost + latency tradeoffs, in-editor.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 其他
你需要填写：EIA_API_KEY, ENTSOE_TOKEN, ELECTRICITYMAPS_TOKEN

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： GREENPICK_CARBON_SOURCE, EIA_API_KEY, ENTSOE_TOKEN, ELECTRICITYMAPS_TOKEN, GREENPICK_CACHE_TTL_SECONDS, GREENPICK_LOG_LEVEL

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "greenpick": {
      "command": "uvx",
      "args": [
        "greenpick"
      ],
      "env": {
        "GREENPICK_CARBON_SOURCE": "your-value",
        "EIA_API_KEY": "your-value",
        "ENTSOE_TOKEN": "your-value",
        "ELECTRICITYMAPS_TOKEN": "your-value",
        "GREENPICK_CACHE_TTL_SECONDS": "your-value",
        "GREENPICK_LOG_LEVEL": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add greenpick --env GREENPICK_CARBON_SOURCE=your-value --env EIA_API_KEY=your-value --env ENTSOE_TOKEN=your-value --env ELECTRICITYMAPS_TOKEN=your-value --env GREENPICK_CACHE_TTL_SECONDS=your-value --env GREENPICK_LOG_LEVEL=your-value -- uvx greenpick

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "greenpick": {
      "command": "uvx",
      "args": [
        "greenpick"
      ],
      "env": {
        "GREENPICK_CARBON_SOURCE": "your-value",
        "EIA_API_KEY": "your-value",
        "ENTSOE_TOKEN": "your-value",
        "ELECTRICITYMAPS_TOKEN": "your-value",
        "GREENPICK_CACHE_TTL_SECONDS": "your-value",
        "GREENPICK_LOG_LEVEL": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "greenpick": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "greenpick"
      ],
      "env": {
        "GREENPICK_CARBON_SOURCE": "your-value",
        "EIA_API_KEY": "your-value",
        "ENTSOE_TOKEN": "your-value",
        "ELECTRICITYMAPS_TOKEN": "your-value",
        "GREENPICK_CACHE_TTL_SECONDS": "your-value",
        "GREENPICK_LOG_LEVEL": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

GREENPICK_CARBON_SOURCE env
Carbon data source: 'auto' (US->EIA, EU->ENTSO-E, Canada->Hydro-Québec/IESO live), 'mock' (offline: typical monthly values from a year of real data), 'eia', 'entsoe', or 'electricitymaps'. Defaults t…
EIA_API_KEY 机密 env
Free US EIA-930 grid data API key (optional; enables live US carbon).
ENTSOE_TOKEN 机密 env
Free EU ENTSO-E grid data token (optional; enables live EU carbon + day-ahead forecast).
ELECTRICITYMAPS_TOKEN 机密 env
Electricity Maps API token (optional; used when GREENPICK_CARBON_SOURCE=electricitymaps).
GREENPICK_CACHE_TTL_SECONDS env
Cache lifetime for fetched grid data, in seconds.
GREENPICK_LOG_LEVEL env
Logging level (DEBUG, INFO, WARNING, ERROR).

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 bithug01 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

星标数多于此分类中其他 32727 个服务器的 0%
发布到注册中心的时间比此分类中其他 32727 个服务器的 95% 更新
基础检查结果等于或优于此分类中其他 32727 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

GreenPick 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：EIA_API_KEY, ENTSOE_TOKEN, ELECTRICITYMAPS_TOKEN

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.6.2）于前天进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Spec Driven Development, Remote Desktop Commander, Foliosio

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者：formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新

安装

Remote Desktop Commander

作者：desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211前天更新

安装

Foliosio

作者：foliosio.com

Read your support inbox, tag conversations and save draft replies from your AI agent. Never sends.

在云端运行今天更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

GreenPick

Write greener, cheaper cloud infrastructure — before you deploy.

A design-time sustainability co-pilot that runs as an MCP server inside your vibe-coding environment (Cursor / Claude Code / Kiro). Ask it where to deploy, when to run a job, or how to right-size — and it answers with an honest carbon + cost + latency tradeoff, in-editor, in seconds. (It responds when you ask; auto-nudging on infra-code changes is on the roadmap.)

Status: working — 19 tools. Live carbon for US (EIA-930), EU (ENTSO-E) and Canada (Hydro-Québec,

IESO — no key needed), cross-cloud cost

parity (Vantage-refreshed), per-axis R/Y/G ratings, carbon-aware scheduling + day-ahead forecast,

GPU-training and LLM-inference carbon, right-sizing, serving-pattern and data-locality/residency

checks — all shipped. Offline or without API keys, falls back to clearly-labelled *typical* values:

this month's average for each grid, from a year of real data.

*The same 40-GPU-hour training run emits ~0.6 kg CO₂ in Stockholm vs ~17.5 kg in Sydney — a ~28× difference from region choice alone (typical October grids; Sydney on Ember's 2025 national figure, a lower bound for its coal-heavier state grid). Ask GreenPick and it surfaces that (with the cost and latency tradeoff) while you're still in the editor, before you commit.*

▶ [See the 5-minute demo](docs/demo.md) — three scenarios with real tool output (training placement, the egress backfire, the forecast).

Features

🧮 **Right-sizing that cuts cost *and* carbon together** — the FinOps win with a carbon twin. The money saved is the reason to run it; the carbon comes free.
⏱ Carbon-aware scheduling — run deferrable jobs (ETL, batch, training) when the grid is cleanest, now or day-ahead. A decision you make for *every* recurring job.
⚡ GPU training & LLM inference carbon — estimate a run's footprint and the greener *where* + *when*; the size/placement choices that actually move the number.
🌍 Greener region picks — ranked by carbon, with the cost + latency tradeoff shown, not hidden.
🛡 Backfire guards — flags when a greener move loses to egress, or crosses a data-residency line.
🔌 Live US + EU grid data, with a realistic fallback built from a year of real data — no API keys required to start.

Why this exists
What it is / is NOT · How it differs
Scope
MCP tools
Data sources
Honest limitations
Quick start
FAQ
Contributing
License

Why this exists

The same web service emits very different CO₂ depending on which region it runs in, because electricity grids differ in carbon intensity. Existing tools either rank regions in the abstract, or measure carbon *after* you deploy. GreenPick does it at the moment you choose, and tells you what the greener choice *costs you* in latency and dollars — so you can accept or reject it with eyes open.

What it is / is NOT

IS: directional "greener choice" guidance for a developer picking a cloud region while building. Guidance, with tradeoffs stated.
IS NOT: audited carbon accounting, ESG/Scope-2 reporting, or a real-time scheduler. GreenPick does not claim GHG-Protocol or ISO compliance — that's what the paid data vendors (Electricity Maps, WattTime) provide. GreenPick is for *decisions*, not *disclosures*.

How it differs from what's out there

Measurement tools (Cloud Carbon Footprint, CodeCarbon, Kepler) report what you *already* emitted, *after* you deploy. GreenPick advises *before* — at the choice.
Cloud providers' own tools (AWS CCFT, Azure, Google's "Low CO₂" region labels) cover *one* cloud and show carbon alone. GreenPick is cross-cloud and weighs carbon against cost and latency so you see the tradeoff, not just a green badge.
CI/CD carbon tools act at merge time on a pipeline job. GreenPick works earlier, in the editor, across more decisions (scheduling, right-sizing, training/inference, egress & residency).
Grid-data SDKs (Electricity Maps, WattTime, GSF Carbon Aware SDK) are building blocks; GreenPick is the decision layer on top of that kind of data.

The combination — cross-cloud, carbon + cost + latency, at design time, in the editor, with backfire guards — is the part nothing else puts together.

Scope

Providers: AWS, GCP, Azure — 25 regions across the US, Europe, Canada, Asia and Australia.
Workloads: long-running web/API services, GPU training runs, and deferrable jobs (ETL / batch / backups) — placement and timing for each.
Carbon = grid carbon intensity by region (directional, point-in-time; live US + EU), plus a day-ahead forecast (EU) for forward-looking scheduling.
Cost = compute + egress ballpark (deterministic static table, apples-to-apples across clouds; never LLM-invented).
Latency = geographic estimate to a stated user location.
Efficiency levers: greener instance family (perf-per-watt), right-sizing (cost + carbon together), and a carbon-metric scaffold to track your app over time.
Guardrails: data-locality / egress backfire checks and cross-jurisdiction residency flags.
Free data only. No flow-traced consumption accounting; carbon forecast is relative, not absolute.

Roadmap: data-format/engine efficiency advice, sub-national grid zones, CI/CD integration. See docs/user-questions-backlog.md.

MCP tools

19 tools, grouped by what you're trying to do.

Pick a region

Tool	What it does
`compare_regions_tri_axis`	Rank candidate regions by carbon with cost + latency tradeoffs; recommend a pick, each axis rated R/Y/G. Pass `regions` to rank only the ones you'd actually consider.
`greener_alternative_to`	"You picked X — the greenest region within +N ms latency is Y (−Z% carbon, Δcost)." The core nudge.
`region_carbon_now`	Current gCO₂/kWh for a region, with source + timestamp + freshness — and, when live, whether that's cleaner, dirtier or about usual for that hour this month.

Time a job (scheduling)

Tool	What it does
`cleanest_hours_to_run`	Cleanest recent hours to time a deferrable job (ETL, backups, training), + % reduction. Pass `window_hours` for a job of known duration → cleanest contiguous window to start at.
`cleanest_time_of_day`	For a recurring job (nightly batch): the region's cleanest time of day, averaged across recent days. A daily pattern, not a 7-day weekly one.
`greenest_regions_on_average`	Which regions are cleanest on a typical day in a given month (or the whole year), ranked from last year's real data. Offline, no keys; a planning view, not today's grid.
`carbon_forecast`	Forward-looking: which upcoming hours will be cleanest, from the EU day-ahead wind+solar forecast (ENTSO-E). Relative ranking; US/uncovered zones degrade to the recent historical pattern.

Estimate a workload's carbon

Tool	What it does
`estimate_workload_carbon`	Rough kgCO₂/month for a workload in a region (an estimate, not a measurement).
`estimate_training_carbon`	Carbon of a GPU training run (TDP-based energy × live grid), with greener where (region ranking) + when (cleanest hours) — the two levers that compound for portable, deferrable training.
`estimate_inference_carbon`	Energy + CO₂ of an LLM inference workload, by model size class × output tokens × requests (× live grid). A low–high band; smaller size class is the big lever. Self-hosted-friendly; not a named-model leaderboard.

Efficiency & guardrails

Tool	What it does
`greener_instance_family`	A greener same-size family in the same region via perf-per-watt (newer generation / ARM Graviton-class does the same work on less energy).
`rightsizing_check`	Right-sizing (the #1 FinOps win) with a carbon twin: if you're over-provisioned, a smaller size cuts both cost and carbon → the right-sized target with monthly $ and kg CO₂ saving.
`offhours_check`	For dev/test/staging that doesn't need to run 24/7: the monthly $ and kg CO2 saved by running it only N hours a week. Compute only, directional.
`data_locality_egress_check`	Warn when running compute away from where your data lives backfires: directional egress $ + coarse transfer carbon, and whether a greener-region move is still a net win.
`migration_payoff`	Does moving the data to the compute's region pay off? Payback in months, one-off vs monthly egress and transfer carbon, net over 12 months. Egress only.
`region_move_justification`	A short paste-ready paragraph for a pull request or decision record, with the carbon, cost, latency and egress numbers and their quality labels. Says so if the move is not an improvement.
`serving_pattern_advice`	For a user-facing platform: serve from one region or several? Traffic-weighted latency + carbon, with the multi-region data-sync egress and cross-jurisdiction residency catches.

Instrument & understand

Tool	What it does
`carbon_metric_scaffold`	Emits paste-in instrumentation (Python) so your app reports its own estimated carbon over time — observable proxies × GreenPick's power model × live grid. Self-tracking, directional; not a runtime meter.
`explain_methodology_and_limits`	The assumptions and what GreenPick explicitly does not claim.

Data sources (free; swappable behind one interface)

EIA-930 (US balancing authorities) and ENTSO-E (EU zones) — live grid carbon, auto-routed by region. Both also drive cleanest_hours_to_run from recent hourly history.
Hydro-Québec (Montreal / Québec City regions) and IESO (Toronto regions) — live hourly generation by source, public, no key needed. Hydro-Québec figures arrive 1.5–6 h late, so the latest complete hour is used and its timestamp shown.
Typical — the offline default and fallback: for each region, this calendar month's average intensity and average hour-of-day profile (UTC), computed from 12 months of real hourly EIA-930 / ENTSO-E data (data/typical.json, refreshed by scripts/refresh_typical.py). Labelled quality: "typical" — a realistic norm, never presented as a current reading.
Annual — for Singapore, Tokyo, Mumbai/Pune and Sydney, where no free hourly data exists: the country's yearly lifecycle grid intensity from Ember (via Our World in Data, CC BY 4.0), with source and year (data/annual.json). Labelled quality: "annual_average"; fine for comparing regions, but carries no timing signal.
**Mock

只显示了开头部分；其余内容请在仓库中阅读

GreenPick

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

需要你填写的值

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“其他”中的其他服务器比较i

常见问题

替代选择

Spec Driven Development

Remote Desktop Commander

Foliosio

作者的 README

Features

Contents

Why this exists

What it is / is NOT

How it differs from what's out there

Scope

MCP tools

Pick a region

Time a job (scheduling)

Estimate a workload's carbon

Efficiency & guardrails

Instrument & understand

Data sources (free; swappable behind one interface)