GreenPick

Name: GreenPick
Author: bithug01

제작자: bithug01버전 0.6.2i★ –i내 컴퓨터에서 실행

레지스트리 이름io.github.bithug01/greenpick

설치

개요

제작자가 쓴 설명(영어 원문)

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

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

사용자 의견i아직 리뷰가 없어요리뷰는 다음 단계에서 계정 기능과 함께 열려요
우리의 기본 점검i1개 중 1개 통과공개된 정보로 점검했으며, 코드는 아직 스캔하지 않았어요
사용 정도i아직 측정 안 함★ –

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

연결 방식: 내 컴퓨터에서 실행
전송 방식: stdio
카테고리: 기타
입력해야 할 값: EIA_API_KEY, ENTSOE_TOKEN, ELECTRICITYMAPS_TOKEN

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

기본 점검: 1개 중 1개 통과
내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
직접 입력할 값: GREENPICK_CARBON_SOURCE, EIA_API_KEY, ENTSOE_TOKEN, ELECTRICITYMAPS_TOKEN, GREENPICK_CACHE_TTL_SECONDS, GREENPICK_LOG_LEVEL

비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.

필요한 것

PyPIPython의 uv 설치(uvx 명령어용)

어떤 앱을 쓰세요?

ClaudeClaude CodeCursorVS CodeChatGPT

위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.

Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요

{
  "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"
      }
    }
  }
}

터미널에서 이 명령어를 실행하세요

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

실행하기 전에 빈 값을 채워 주세요.

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

{
  "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"
      }
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

{
  "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"
      }
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.

선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.

직접 입력할 값

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 비밀 env
Free US EIA-930 grid data API key (optional; enables live US carbon).
ENTSOE_TOKEN 비밀 env
Free EU ENTSO-E grid data token (optional; enables live EU carbon + day-ahead forecast).
ELECTRICITYMAPS_TOKEN 비밀 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).

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검

✓ 통과
제작자와 저장소 일치
레지스트리 이름이 GitHub 계정 bithug01 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
… 아직 점검 안 함
계속 관리되고 있음
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
… 아직 점검 안 함
라이선스 있음
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
– 해당 없음
엔드포인트 응답
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
– 해당 없음
OAuth로 로그인
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
… 아직 점검 안 함
도구 설명에 숨겨진 지시 없음
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요

기타 카테고리의 다른 서버와 비교i

이 카테고리의 다른 서버 18333개 중 0%보다 스타가 많아요
이 카테고리의 다른 서버 18333개 중 95%보다 최근에 레지스트리에 공개됐어요
기본 점검 결과가 이 카테고리의 다른 서버 18333개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

GreenPick, 안전한가요?

해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.

어떤 키나 코드가 필요한가요?

입력해야 할 값: EIA_API_KEY, ENTSOE_TOKEN, ELECTRICITYMAPS_TOKEN

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.6.2)은 그저께 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 있어야 해요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, Signoff

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Spec Driven Development

제작자: formulahendry

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

내 컴퓨터에서 실행★ 440MIT14개월 전 업데이트

설치

Remote Desktop Commander

제작자: desktop-commander

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

클라우드에서 실행★ 211그저께 업데이트

설치

Signoff

제작자: ayuanxx-max

Five gates that make an agent's 'done' provable: run them, read the verdict, audit the record.

내 컴퓨터에서 실행오늘 업데이트

설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

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

앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요

GreenPick

개요

PopNx MCP의 세 기둥

할 수 있는 일

설치

설치하기 전에

직접 입력할 값

할 수 있는 일

기본 점검i

제작자와 저장소 일치

계속 관리되고 있음

라이선스 있음

엔드포인트 응답

OAuth로 로그인

도구 설명에 숨겨진 지시 없음

기타 카테고리의 다른 서버와 비교i

자주 묻는 질문

대안

Spec Driven Development

Remote Desktop Commander

Signoff

제작자의 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)