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CloudEval turns cloud context and infrastructure-as-code into reports, diagrams, grounded answers, and automation-ready artifacts across the browser, terminal, CI, and MCP-compatible agents. Use it when you need 1,700+ cloud evaluation signals, shareable reports, a GitHub review workflow, and agent-ready context without rebuilding the review stack yourself.
Want to inspect the workflow before connecting anything private? Start with the public sample, then move into CLI, Web, GitHub, or MCP setup.

Video walkthrough: CloudEval turns cloud and infrastructure-as-code context into reviewable reports, diagrams, and agent-ready answers.

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The problem

Cloud reviews break down when the evidence needed for a shipping decision lives across templates, cloud consoles, cost views, architecture diagrams, security findings, and pull requests.

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The CloudEval review loop

CloudEval turns live cloud context, infrastructure-as-code, and repository provenance into one review surface with diagrams, cost signals, Well-Architected findings, MCP-compatible agent context, and automation-ready gates.

Use the same review context in the tools your team already has open.

Model Context Protocol logoMCPExpose Cloudeval tools and project context to compatible clients.
OpenAI logoCodexGive coding agents cloud evidence, reports, and deeplinks.
Cursor logoCursorReview infrastructure changes from the editor with project context.
Claude logoClaude CodeUse Cloudeval evidence inside MCP-aware terminal workflows.
Visual Studio Code logoVS CodeBring review context into workspace MCP configuration.
>_CLIRun reports, ask questions, and script review output.
GitHub logoGitHub ActionsPost PR comments, upload artifacts, and enforce gates.

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What you get

CLI and Web workspace

Run reports, ask grounded questions, open deeplinks, and keep the browser for visual review.

1,700+ evaluation signals

Turn cloud and IaC inputs into architecture, security, reliability, cost, diagram, and graph checks.

Any agent, anywhere

Use MCP, llms.txt, headless exports, and CLI output with ChatGPT, Claude, Codex, Cursor, and VS Code.

GitHub App and CI review

Sync repositories, run pull-request reviews, post comments, upload artifacts, and fail gates in CI.
CloudEval workspace with AI chat prompts next to an architecture diagram with issue badges, cost, report state, and share controls

CloudEval keeps project-aware AI chat next to the architecture graph, source context, report state, and share controls.

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Start with one path

Try public sample

Inspect a real example repo and demo PRs before setup.

Use the CLI

Install the CLI, run reports, ask questions, and automate reviews.

Review pull requests

Put CloudEval review comments, artifacts, and gates inside GitHub Actions.

Connect a source

Choose Cloud sync, a template, a workspace, or a GitHub-backed project.

Set up MCP

Give compatible agents CloudEval tools, context, and deeplinks.

Check coverage

See what is current, in progress, planned, and intentionally limited.

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Next step

Open How CloudEval works to learn the platform model, or jump straight to the quickstart if you want hands-on setup.
Last modified on July 11, 2026