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Documentation Index

Fetch the complete documentation index at: https://docs.cloudeval.ai/llms.txt

Use this file to discover all available pages before exploring further.

This is the fastest path if you want CloudEval in a terminal, CI job, or internal automation.

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When this path fits

Use the CLI when you want to:
  • Create or inspect projects without living in the browser
  • Run reports and capture machine-readable output
  • Ask one grounded question in a script
  • Open exact app pages for projects, reports, or billing views
If you mainly need visual diagram review or sharing, the web app is still the better primary surface.
CloudEval command line panel with install command, start commands, and agent handoff actions

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Use the CLI in six steps

1

Install the CLI

Install the latest release:
curl -fsSL https://cli.cloudeval.ai/install.sh | bash
The installer may offer to install shell tab completions for your login shell (bash, zsh, or fish). To skip that step, run CLOUDEVAL_INSTALL_COMPLETION=0 curl -fsSL https://cli.cloudeval.ai/install.sh | bash.CloudEval ships two binary names:
  • cloudeval
  • eva
Verify the install:
cloudeval --version
eva --version
2

Configure a profile

Store local defaults for the backend, frontend, default project, and model. Profiles are useful for agents, CI jobs, and machines that work with more than one environment.
cloudeval setup \
  --non-interactive \
  --profile quickstart \
  --model gpt-5-nano \
  --format json
Check the profile and local environment:
cloudeval config show --profile quickstart --format json
cloudeval status --profile quickstart --format json
cloudeval doctor --profile quickstart --format json
3

Enable shell completion

Print the completion script for your shell:
cloudeval completion bash
cloudeval completion zsh
cloudeval completion fish
cloudeval completion powershell
Install completion to a standard per-user path:
cloudeval completion install --shell bash
cloudeval completion install --shell zsh
cloudeval completion install --shell fish
cloudeval completion install --shell powershell
Remove installed completion:
cloudeval completion uninstall --shell bash
cloudeval completion uninstall --shell zsh
cloudeval completion uninstall --shell fish
cloudeval completion uninstall --shell powershell
4

Sign in

Choose the login mode based on where the CLI is running.
Use normal browser login when you are running the CLI on your own laptop or desktop and the terminal can open a browser on the same machine.
cloudeval login
cloudeval auth status
Headless login is not a different account type. It is only a login flow for terminals that cannot complete the normal browser handoff.
5

Create a project

If your source of truth is Bicep, compile it to ARM JSON first. The CLI’s strongest IaC import path today is ARM JSON.
Download an Azure Quickstart template, then create a project from the local file:
curl -fsSL \
  https://raw.githubusercontent.com/Azure/azure-quickstart-templates/master/quickstarts/microsoft.compute/1vm-2nics-2subnets-1vnet/azuredeploy.json \
  -o ./azuredeploy.json

cloudeval projects create \
  --template-file ./azuredeploy.json \
  --name "Azure quickstart import" \
  --provider azure \
  --profile quickstart \
  --format json \
  --output ./cloudeval-project.json
The JSON response includes the created project and connection metadata. Capture both IDs for follow-up commands:
PROJECT_ID=$(jq -r '.data.project.id' ./cloudeval-project.json)
CONNECTION_ID=$(jq -r '.data.connection.id' ./cloudeval-project.json)

cloudeval projects get "$PROJECT_ID" --profile quickstart --format json
cloudeval connections get "$CONNECTION_ID" --profile quickstart --format json
6

Run reports

cloudeval reports run \
  --project "$PROJECT_ID" \
  --type all \
  --wait \
  --profile quickstart \
  --format json
Fetch a human-readable cost summary:
cloudeval reports cost \
  --project "$PROJECT_ID" \
  --profile quickstart \
  --format markdown
Fetch a human-readable architecture summary:
cloudeval reports waf \
  --project "$PROJECT_ID" \
  --profile quickstart \
  --format markdown
Download saved report payloads for another tool or CI artifact:
cloudeval reports download \
  --project "$PROJECT_ID" \
  --type all \
  --view parsed \
  --output ./reports \
  --profile quickstart \
  --format json \
  --non-interactive
7

Open the project or ask a question

Open the diagram:
cloudeval open project "$PROJECT_ID" \
  --view preview \
  --layout architecture \
  --print-url \
  --no-open
Ask one grounded question:
cloudeval ask "Summarize the top cost and architecture risks in this project" \
  --project "$PROJECT_ID" \
  --profile quickstart \
  --format json \
  --non-interactive
Use ask when you want one answer back. Use chat when you want an ongoing interactive session.

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Inspect models and sessions

List backend-supported models and set a default: 
cloudeval models list --profile quickstart --format json
cloudeval models default set gpt-5-nano --profile quickstart --format json
After an ask run, inspect local session history: 
cloudeval sessions list --profile quickstart --format json
cloudeval sessions get <thread-id> --profile quickstart --format json
You can also search, rename, and resume recent work: 
cloudeval sessions search "cost risk" --profile quickstart --format json
cloudeval sessions rename <thread-id> "Cost and architecture review" --profile quickstart --format json
cloudeval chat --resume "Cost and architecture review" --profile quickstart
For repeatable one-shot follow-ups, reuse a thread ID: 
cloudeval ask "What changed since the previous report?" \
  --thread <thread-id> \
  --project "$PROJECT_ID" \
  --profile quickstart \
  --format json \
  --non-interactive

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Run CloudEval as an MCP server

If your agent framework already supports MCP, you can expose CloudEval as a local tool server instead of calling one CLI command at a time. 
cloudeval mcp status --format json
cloudeval doctor --mcp --format json
codex mcp add cloudeval -- cloudeval mcp serve
CloudEval can generate setup instructions for common MCP clients: 
cloudeval mcp setup codex --dry-run
cloudeval mcp setup claude --dry-run
cloudeval mcp setup cursor --dry-run
cloudeval mcp setup generic --dry-run --toolset readonly --format json
Use a focused toolset when an agent only needs part of the CloudEval surface: 
cloudeval mcp serve --toolset readonly
cloudeval mcp serve --toolset reports
cloudeval mcp serve --toolset billing
For custom MCP clients, the equivalent JSON shape is: 
{
  "mcpServers": {
    "cloudeval": {
      "command": "cloudeval",
      "args": ["mcp", "serve"]
    }
  }
}
For Ollama-powered agents, add this CloudEval MCP entry to the MCP-capable agent host that Ollama launches. The CloudEval server still runs over standard stdio MCP. Use stored cloudeval login auth, stored cloudeval login --headless auth, or --machine. Run login before starting mcp serve; stdin is reserved for MCP protocol messages. MCP clients that support richer discovery can also see CloudEval resources and prompt templates for capabilities, projects, billing summaries, report review, cost review, architecture review, and billing review.

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Safe defaults for automation

  • Run cloudeval capabilities --format json before relying on command names in automation.
  • Run cloudeval doctor --format json before relying on a local install in automation.
  • Run cloudeval doctor --mcp --format json before relying on MCP setup.
  • Prefer --format json for scripts.
  • Prefer --non-interactive in CI or agent workflows.
  • Prefer --profile <name> when multiple agents or environments share one machine.
  • Prefer --print-url --no-open when a command can generate a CloudEval app link.
  • Use cloudeval login --headless when you are not on a workstation with a usable browser.
  • Use --machine only when service-principal machine authentication is configured.

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Next step

Use CLI overview for the command model, CLI command reference for the current surface area, or Automate evaluations with the CLI for repeatable workflows.
Last modified on May 9, 2026