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Cloudeval AI

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time — check back soon.
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Cloud evaluations and reviews for Azure and static AWS CloudFormation (beta). Requires an account.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON ▾
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for Cloudeval AI, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
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Documentation Overview

Cloudeval AI

Cloudeval CLI terminal UI

Cloudeval CLI

Your cloud, in the terminal: evaluated, reported, and agent-ready.

Latest release npm version npm downloads GitHub downloads Release health Cloudeval Docs Discord Issues AGENTS.md

Cloudeval CLI turns ARM templates, GitHub-hosted IaC, and live Azure context into cost, architecture, and Well-Architected signals. Use it as a terminal UI, a scriptable automation client, or an MCP server for Codex, Cursor, Claude, VS Code, and any stdio JSON-RPC client.

Why Use It

ForWhat you get
Terminal usersA full TUI with chat, Agent mode, workspace tabs, thread switching, slash commands, a context rail, task ledger, artifact chips, and local SQLite session history.
AutomationStable json, ndjson, markdown, and text output; payloads on stdout; progress and prompts on stderr; predictable exit codes.
Agents and CIScoped access-key credentials, redacted output by default, MCP toolsets, recipes, and machine-readable capability metadata.

Install

Node.js 20+ users can install from npm:

Terminal
npm install -g @ganakailabs/cloudeval-cli
cloudeval --help

macOS, Linux, WSL2, Git Bash, and PowerShell 7+ on Windows or Linux can use the standalone release installer:

Terminal
curl -fsSL https://cli.cloudeval.ai/install.sh | bash
powershell
irm https://cli.cloudeval.ai/install.ps1 | iex

Then reload your shell and sign in:

bash
source ~/.bashrc   # or: source ~/.zshrc
cloudeval login
cloudeval status
cloudeval chat

Device login goes through cloudeval.ai and always asks the browser auth provider to show the account chooser, so users can choose the intended work email even when another account is already signed in. No local Azure app registration is needed for normal CLI use.

Installer behavior and controls

The installer:

  • downloads checksum-verified GitHub release assets and installs cloudeval;
  • creates the eva and cloud aliases on non-Windows platforms;
  • can install shell completions for bash, zsh, and fish;
  • can offer concise MCP setup for detected Codex, Claude Desktop, Cursor, and VS Code clients, skipping clients where Cloudeval MCP is already configured and avoiding prompts when only manual-only setup remains;
  • asks whether to share limited CLI telemetry, defaulting to yes; declining writes telemetry.enabled=false;
  • explains credential setup but does not create access keys or write secrets into MCP client config;
  • shows compact labeled progress bars in interactive terminals;
  • uses connect/stall timeouts so slow CDN transfers fail clearly.

Useful controls:

Terminal
curl -fsSL https://cli.cloudeval.ai/install.sh | CLOUDEVAL_INSTALL_AGENT_SETUP=0 bash
curl -fsSL https://cli.cloudeval.ai/install.sh | CLOUDEVAL_INSTALL_MCP_CLIENTS=codex,cursor bash
curl -fsSL https://cli.cloudeval.ai/install.sh | CLOUDEVAL_TELEMETRY=0 bash
powershell
$env:CLOUDEVAL_ASSUME_YES = "1"
irm https://cli.cloudeval.ai/install.ps1 | iex

The bash installer can also detect agent clients and offer MCP setup. The PowerShell installer installs the verified binary, yoga.wasm, license notices, PATH, and optional PowerShell tab completions. Run cloudeval mcp setup afterward when you want MCP client configuration.

Telemetry

Cloudeval CLI sends curated custom events to Azure Application Insights by default. Events cover command family, success, duration, safe option enums, CLI version, Node/runtime version, OS major version, architecture, install source, update/install outcomes, MCP tool names, and TUI launch/exit metadata. After login, events may include the signed-in email and first/last/full name.

Telemetry never sends raw prompts, command output, tokens, local paths, project or resource identifiers, account/session/tenant identifiers, cloud resource names, stack traces, or raw error messages. Disable or re-enable it anytime:

bash
cloudeval config set telemetry.enabled false
cloudeval config get telemetry.enabled --format json
cloudeval config set telemetry.enabled true
cloudeval config unset telemetry.enabled

Environment overrides take precedence for a single run:

bash
CLOUDEVAL_TELEMETRY=0 cloudeval status --format json
CLOUDEVAL_TELEMETRY=1 cloudeval --help

Update later with:

bash
cloudeval update --check
cloudeval update --yes

After an update, restart or reload configured MCP clients when you are ready to load newly exposed Cloudeval tools, resources, or prompts. Cloudeval does not restart Codex, Claude, Cursor, VS Code, or other MCP hosts automatically.

Uninstall local installer-owned artifacts while keeping Cloudeval config, sessions, and auth by default:

bash
cloudeval uninstall --dry-run
cloudeval uninstall --yes
cloudeval uninstall --yes --remove-config  # also removes ~/.config/cloudeval
npm uninstall -g @ganakailabs/cloudeval-cli # if installed through npm

Start Here

bash
cloudeval                         # Terminal UI
cloudeval tui --graph-diagram ascii
cloudeval ask "Summarize my cloud risk" --format json
cloudeval agent "Find cost and architecture risks" --format json
cloudeval agents list
cloudeval agents run cost --project <project-id> --format json
cloudeval recipes list
cloudeval projects list
cloudeval uninstall --dry-run
cloudeval projects graph insights <project-id> --focus impact --resource <resource-id> --format json
cloudeval validate template --template-file template.json --parameters-file parameters.json --rule <check-id> --details --wait --progress stderr --wait-timeout 600000 --format json
cloudeval validate tests --template-file template.json --parameters-file parameters.json --wait --progress stderr --wait-timeout 600000 --format json
cloudeval rules search "public network" --format json
cloudeval reports list
cloudeval actions list --type architecture,cost,unit-tests --format json
cloudeval actions open --print-url --no-open
cloudeval review --repo owner/repo --ref feature/infra-change --commit-sha <sha> --github-checks --sarif --output cloudeval-review --format json --non-interactive
cloudeval capabilities --format json
cloudeval doctor --deep

Full docs: Get started with the CLI and CLI command reference.

Read the full README →View source on GitHub →

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Reviews

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Frequently Asked Questions about Cloudeval AI

We don't have a confirmed install command for Cloudeval AI yet, so we don't publish a generated one — a guessed package name would point at the wrong package or none at all. Follow the project's own README or setup instructions (https://github.com/ganakailabs/cloudeval-cli) for the current steps.

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Technical Specs & Signals

Category☁️Cloud Platforms
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Last updatedSep 28, 2026
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27Quality signal: Emerging · 27/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools11/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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