Cloudcostsmcp

Name: Cloudcostsmcp
Author: x7even

제작자: x7even버전 1.0.2i★ –i내 컴퓨터에서 실행

레지스트리 이름io.github.x7even/cloudcostsmcp

설치

개요

제작자가 쓴 설명(영어 원문)

Anchor AI FinOps to real, live cloud pricing — AWS, GCP & Azure public and enterprise rates.

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: 개발자 도구
입력해야 할 값: OCC_GCP_API_KEY, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY

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: OCC_GCP_API_KEY, OCC_AWS_ENABLE_COST_EXPLORER, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, OCC_GCP_BILLING_ACCOUNT_ID

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": {
    "cloudcostsmcp": {
      "command": "uvx",
      "args": [
        "opencloudcosts"
      ],
      "env": {
        "OCC_GCP_API_KEY": "your-value",
        "OCC_AWS_ENABLE_COST_EXPLORER": "your-value",
        "AWS_ACCESS_KEY_ID": "your-value",
        "AWS_SECRET_ACCESS_KEY": "your-value",
        "OCC_GCP_BILLING_ACCOUNT_ID": "your-value"
      }
    }
  }
}

터미널에서 이 명령어를 실행하세요

claude mcp add cloudcostsmcp --env OCC_GCP_API_KEY=your-value --env OCC_AWS_ENABLE_COST_EXPLORER=your-value --env AWS_ACCESS_KEY_ID=your-value --env AWS_SECRET_ACCESS_KEY=your-value --env OCC_GCP_BILLING_ACCOUNT_ID=your-value -- uvx opencloudcosts

실행하기 전에 빈 값을 채워 주세요.

Cursor에서 열기

또는 설정을 직접 복사하기

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

{
  "mcpServers": {
    "cloudcostsmcp": {
      "command": "uvx",
      "args": [
        "opencloudcosts"
      ],
      "env": {
        "OCC_GCP_API_KEY": "your-value",
        "OCC_AWS_ENABLE_COST_EXPLORER": "your-value",
        "AWS_ACCESS_KEY_ID": "your-value",
        "AWS_SECRET_ACCESS_KEY": "your-value",
        "OCC_GCP_BILLING_ACCOUNT_ID": "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": {
    "cloudcostsmcp": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "opencloudcosts"
      ],
      "env": {
        "OCC_GCP_API_KEY": "your-value",
        "OCC_AWS_ENABLE_COST_EXPLORER": "your-value",
        "AWS_ACCESS_KEY_ID": "your-value",
        "AWS_SECRET_ACCESS_KEY": "your-value",
        "OCC_GCP_BILLING_ACCOUNT_ID": "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

OCC_GCP_API_KEY 비밀 env
GCP free API key — required for GCP pricing, no billing account needed.
OCC_AWS_ENABLE_COST_EXPLORER env
Set to true to enable effective/discounted AWS pricing via Cost Explorer ($0.01/call).
AWS_ACCESS_KEY_ID 비밀 env
AWS credentials — only needed for effective pricing (RI/Savings Plans). Public pricing works without credentials.
AWS_SECRET_ACCESS_KEY 비밀 env
AWS secret key — paired with AWS_ACCESS_KEY_ID for effective pricing.
OCC_GCP_BILLING_ACCOUNT_ID env
GCP billing account ID — enables effective/contract pricing via Cloud Billing API.

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 6일 점검

✓ 통과
제작자와 저장소 일치
레지스트리 이름이 GitHub 계정 x7even 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

개발자 도구 카테고리의 다른 서버와 비교i

이 카테고리의 다른 서버 1390개 중 0%보다 스타가 많아요
이 카테고리의 다른 서버 1390개 중 29%보다 최근에 레지스트리에 공개됐어요
기본 점검 결과가 이 카테고리의 다른 서버 1390개 중 100%와 같거나 더 좋아요

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

자주 묻는 질문

Cloudcostsmcp, 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: OCC_GCP_API_KEY, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY

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(1.0.2)은 4개월 전 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: GitHub, Desktop Commander, Cloudflare MCP

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

GitHub

제작자: github

Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.

컴퓨터 또는 클라우드★ 34,245MIT3일 전 업데이트

설치

Desktop Commander

제작자: wonderwhy-er

MCP server for terminal commands, file operations, and process management

내 컴퓨터에서 실행★ 10,225MIT15일 전 업데이트

설치

Cloudflare MCP

제작자: cloudflare

Cloudflare MCP servers

클라우드에서 실행★ 4,446Apache-2.014개월 전 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.

OpenCloudCosts MCP

Anchor AI FinOps to real, live cloud pricing.

Your LLM's cloud pricing knowledge was frozen at training cutoff. Cloud pricing wasn't.

opencloudcosts is an MCP server that gives your AI assistant live, structured access to AWS, GCP, and Azure pricing — 16 tools it can call directly, with results it can reason over rather than guess at. Ask it to compare a full multi-resource workload across all three clouds simultaneously. Ask it for your actual post-discount effective rates from Reserved Instances, Savings Plans, or Enterprise Discount Programs. Ask it to fan out across every region and return the cheapest option. None of that is possible from training data alone, and none of it is possible from a single-cloud pricing calculator.

The Problem

Ask any AI assistant what an m5.2xlarge costs in ap-southeast-2, whether AWS or GCP is cheaper for a three-tier web app, or what your effective hourly rate is after Savings Plans. You will get a confident answer. It will probably be wrong.

Cloud pricing is a poor fit for static model weights: instance families are added and retired, regional pricing diverges (the same instance type can differ 20–40% across regions), spot markets fluctuate, and commitment discounts are by definition unique to each account. A model answering from training data cannot know your negotiated EDP rate. It can barely reliably recall last year's list price. The problem is not that LLMs are bad — it's that real-time, account-specific pricing data is structurally outside what a model can know.

opencloudcosts fixes this by giving your AI assistant 16 MCP tools backed by live provider APIs. Instead of hallucinating numbers, the model calls a tool and gets a precise answer.

A Concrete Scenario

Your team asks: *"We're evaluating whether to migrate this workload from AWS to GCP. What does the full stack cost on-demand vs. three-year committed, across all three clouds, so I can make the case to leadership?"*

Without this tool, the model interpolates — possibly from pricing that is a year out of date — across three providers, multiple resource types, and at least four pricing tiers. The numbers will be plausible and wrong.

With opencloudcosts, the model calls compare_bom with the workload spec (compute + storage + database). The tool fans out 8 concurrent provider calls across AWS, GCP, and Azure simultaneously, prices each resource category at public and committed rates, and returns a per-provider, per-term breakdown with savings analysis — in a single tool call.

No spreadsheet. No switching between three provider calculators. No manual SKU matching. No training-data approximation.

Why Not the Obvious Alternatives

Asking a model without tools — Training data has a cutoff. Cloud pricing changes constantly, varies 20–40% across regions for the same instance type, and includes account-level commitment discounts that are invisible at inference time. The model will approximate, confabulate, or recall stale numbers. There is no structured output, and the model cannot access your contracted rates under any circumstances.

Cloud pricing calculators (AWS / GCP / Azure) — Each covers exactly one cloud. They are UI-only with no API surface callable from an AI assistant. Cross-cloud comparison requires manually reproducing the same architecture three times across three separate calculators and reconciling exports by hand. They have no concept of unit economics and produce no programmatic output for agentic workflows.

Infracost — Excellent at estimating cost diffs against Terraform plans. It requires IaC files as input — it cannot answer "what does an n2-standard-8 cost in europe-west4?" without a Terraform plan in hand. It is not an MCP server and is not callable from a conversational AI context.

Calling provider APIs directly — AWS bulk pricing files are multi-GB and require targeted API access patterns to avoid downloading them in full. GCP's Cloud Billing Pricing API v1beta requires a multi-source ADC credential chain and provider-specific IAM. Azure's Retail Prices API needs pagination logic and SKU matching. Every provider uses different region naming conventions, SKU formats, and data schemas. opencloudcosts normalizes all of this behind a uniform MCP tool interface and handles credential chains, caching, and retry logic so the model does not have to.

Capabilities at a Glance

Capability	LLM (no tools)	Cloud Calculators	Infracost	opencloudcosts
Live pricing (not frozen at training cutoff)	No	Manual input only	IaC-bound	Yes — fetched from provider APIs
Cross-cloud comparison in one call	No	No	No	Yes — `compare_bom`, 8 concurrent provider calls
Effective/contracted rates (RI, SP, EDP, CUD)	No	No	No	Yes — credentials unlock this layer
Multi-region concurrent fan-out	No	No	No	Yes — up to 32 goroutines
Unit economics (cost/user, cost/request)	Approximate	No	No	Yes — `estimate_unit_economics`
MCP tool surface — callable by model	N/A	No	No	Yes — 16 tools
AWS + Azure public pricing, zero credentials	N/A	Single-cloud only	Partial	Yes
HTTP service for shared/Kubernetes deployments	N/A	N/A	No	Yes — bearer auth, rate limiting, probes

Coverage: Three Clouds, 16 Tools

Fourteen tools are fully functional. Two entries are compatibility stubs: search_pricing (a deprecated redirect from v0.8.x, kept for backward compatibility) and get_spot_history (registered but not implemented in the Go server — returns a structured "not available" response).

Credential requirements by provider

Coverage	Credentials required
AWS EC2, EBS (gp3/io2/sc1), RDS, inter-region egress	None
Azure VMs, Managed Disks, Blob Storage, Azure SQL/MySQL/PostgreSQL, Cosmos DB, AKS, Azure Functions, Azure OpenAI (GPT-4o, GPT-4, GPT-3.5-Turbo, o1, o1-mini, embeddings)	None — fully public Retail Prices API
GCP Compute Engine, Cloud Storage, Persistent Disk, Cloud SQL, Memorystore, GKE, BigQuery, Vertex AI, Gemini, Cloud LB/CDN/NAT/Armor, Cloud Monitoring	Free API key (`OCC_GCP_API_KEY`) — no billing account, no credit card
AWS post-discount rates (Reserved Instances, Savings Plans) + `get_discount_summary`	AWS credentials + `OCC_AWS_ENABLE_COST_EXPLORER=true` ($0.01/call to Cost Explorer, opt-in only)
GCP committed-use discounts (CUDs) and Enterprise Discount Programs (EDPs)	ADC credentials + `billing.billingAccountPrice.get` IAM + `OCC_GCP_BILLING_ACCOUNT_ID`

Azure Reserved VM pricing (1-year and 3-year terms) is available via the public Retail Prices API — no credentials needed. compare_bom returns committed-term Azure pricing with no setup beyond the binary.

The 16 tools by category

Category	Tools
Pricing	`get_price`, `get_prices_batch`, `compare_prices`, `describe_catalog`, `search_pricing`†, `get_spot_history`†
FinOps	`estimate_bom`, `estimate_unit_economics`, `compare_bom`, `get_discount_summary`
Discovery	`list_regions`, `list_instance_types`, `find_cheapest_region`, `find_available_regions`
Cache	`refresh_cache`, `cache_stats`

† Compatibility stub only — not functional for live data. See opencloudcosts-go/README.md for full parameter reference.

Performance and Reliability

Concurrency — The analysis tools are not sequential HTTP wrappers:

find_cheapest_region and find_available_regions: errgroup + semaphore, up to 32 goroutines — queries all available regions in parallel, returns results sorted cheapest-first
compare_bom: 8 concurrent provider calls across AWS, GCP, and Azure simultaneously
compare_prices: semaphore of 10 concurrent region calls
get_prices_batch: parallelized across instance types within a region

Rate limiting and timeouts — Token-bucket rate limiter at 200 req/s on the HTTP transport (OCC_RATE_LIMIT). Per-request deadline: 60s (OCC_REQUEST_TIMEOUT). Per-provider API call: 30s (OCC_PROVIDER_TIMEOUT). Graceful SIGTERM drain: 15s (OCC_SHUTDOWN_TIMEOUT).

Cache — Prices are stored in a concurrent in-memory cache (read-optimised with sync.RWMutex) with atomic JSON persistence at ~/.cache/opencloudcosts/cache.json. TTLs: public prices 24h (OCC_CACHE_TTL_HOURS), region/instance metadata 7 days (OCC_METADATA_TTL_DAYS), effective/contracted rates 1h (OCC_EFFECTIVE_PRICE_TTL_HOURS). Cache survives binary updates. 401/403 responses from billing APIs are never cached, so credential rotation takes effect immediately.

AWS pricing — EC2/EBS/RDS public pricing uses a targeted SKU API path rather than downloading the full multi-GB bulk pricing file, keeping startup fast and avoiding large network payloads.

Error isolation — Raw exception text never reaches LLM context. All tool boundaries emit structured error envelopes; full tracebacks are logged server-side only. GCP contract pricing falls back to public list prices on auth failure rather than surfacing an error into the conversation.

Validated: 234/234

opencloudcosts v1.0.0 achieves 234/234 (100%) on the LLM grounding harness, with zero XML hallucinations across the full suite.

The harness covers 234 realistic pricing questions across all three clouds: instance spot checks, cross-region comparisons, BOM estimates, multi-cloud comparisons, unit economics, AI model pricing, database pricing, storage pricing, discount summaries, egress pricing, availability queries, and network pricing.

Primary validation model: `qwen3.6-35b-128k` running locally via llama-swap — a self-hosted 35B reasoning model with no external API dependency. The harness has also been exercised against qwen3.6-35b-a3b, qwen3.5-122b-a10b@q6_k, and gemma-4-26b-a4b. Because MCP is a protocol rather than a model feature, accuracy comes from the tool returning correct live data — any MCP-capable AI assistant calls the same tool surface and gets the same structured response.

Harness progression: 109/123 (Python v0.8.x) → 169/169 (v0.9.0) → 199/199 (v0.9.2) → 234/234 (v1.0.0).

643 Go unit tests across all providers and tools verify behavioral correctness and cross-provider API parity, independent of LLM evaluation.

Use Cases

1. Price the same workload across all three clouds at once

"Price the following on AWS, GCP, and Azure simultaneously: 4 instances (8 vCPU, 32 GB RAM), 1 managed PostgreSQL database (4 vCPU, 16 GB RAM), and 500 GB block storage. Return on-demand, 1-year committed, and 3-year committed totals for each cloud with monthly and annual figures, and flag which provider is cheapest at each commitment term."

compare_bom fans out 8 concurrent provider calls and returns a per-provider, per-term breakdown with savings analysis versus on-demand. No pricing calculator does this across cloud boundaries. AWS and Azure public pricing requires no credentials; GCP requires a free API key.

2. Find the cheapest AWS region for a long-running compute workload

"I need to run a c6a.4xlarge continuously. Fan out across all available AWS regions and return the 5 cheapest, sorted by on-demand hourly rate. Show us-east-1 as a baseline."

find_cheapest_region uses a 32-goroutine fan-out across every region where the instance type is available and returns results sorted cheapest-first. Regional price deltas for the same instance type routinely exceed 20%. No credentials needed.

3. Determine your effective AWS rate after commitments

"I h

앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요

Cloudcostsmcp

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

개발자 도구 카테고리의 다른 서버와 비교i

자주 묻는 질문

대안

GitHub

Desktop Commander

Cloudflare MCP

제작자의 README

OpenCloudCosts MCP

Anchor AI FinOps to real, live cloud pricing.

The Problem

A Concrete Scenario

Why Not the Obvious Alternatives

Capabilities at a Glance

Coverage: Three Clouds, 16 Tools

Performance and Reliability

Validated: 234/234

Use Cases