Cloudcostsmcp

Name: Cloudcostsmcp
Author: x7even

by x7evenversion 1.0.2i★ –iRuns on your computer

Registry nameio.github.x7even/cloudcostsmcp

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Developer tools
You need to fill in: OCC_GCP_API_KEY, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: OCC_GCP_API_KEY, OCC_AWS_ENABLE_COST_EXPLORER, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, OCC_GCP_BILLING_ACCOUNT_ID

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "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"
      }
    }
  }
}

Run this in your terminal

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

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "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"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "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"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

OCC_GCP_API_KEY secret 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 secret env
AWS credentials — only needed for effective pricing (RI/Savings Plans). Public pricing works without credentials.
AWS_SECRET_ACCESS_KEY secret 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.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account x7even, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

More stars than 0% of the other 1393 servers in this category
Published to the registry more recently than 29% of the other 1393 servers in this category
Basic checks equal to or better than 100% of the other 1393 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Cloudcostsmcp safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: OCC_GCP_API_KEY, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.2) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

Cloudcostsmcp

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Developer toolsi

Common questions

Alternatives

GitHub

Desktop Commander

Cloudflare MCP

README from the maker

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