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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 page is for teams that want CloudEval to fit into existing engineering workflows instead of being a browser-only step.

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Before you automate

  • Authenticate first with cloudeval login or cloudeval login --headless.
  • Run cloudeval capabilities --format json before hard-coding commands in a new automation.
  • Run cloudeval doctor --format json before relying on a local install in automation.
  • Run cloudeval doctor --mcp --format json before wiring CloudEval into an MCP-compatible agent.
  • Prefer --format json --non-interactive --profile <name> --print-url --no-open unless a workflow explicitly needs browser behavior.

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Configure an automation profile

Profiles keep defaults isolated for agents, CI jobs, and environments: 
cloudeval setup \
  --non-interactive \
  --profile nightly \
  --model gpt-5-nano \
  --format json

cloudeval status --profile nightly --format json
cloudeval doctor --profile nightly --format json
Add --base-url, --frontend-url, and --project when your automation should pin those defaults: 
cloudeval setup \
  --non-interactive \
  --profile nightly \
  --base-url https://cloudeval.ai/api/proxy/v1 \
  --frontend-url https://cloudeval.ai \
  --project <project-id> \
  --model gpt-5-nano \
  --format json

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Import ARM JSON from a local file

Use --template-file when your pipeline already has an ARM template on disk. This also works when an agent or previous step downloaded an Azure Quickstart template into the workspace. 
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 "Nightly evaluation" \
  --provider azure \
  --profile nightly \
  --format json \
  --output ./cloudeval-project.json
If the file already exists locally, point the CLI at that file instead: 
cloudeval projects create \
  --template-file ./.cloudeval-downloads/1vm-2nics-2subnets-1vnet.azuredeploy.json \
  --name "Nightly evaluation" \
  --provider azure \
  --profile nightly \
  --format json \
  --output ./cloudeval-project.json

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Import from an Azure Quickstart GitHub URL

Use --template-url when the source of truth should stay in GitHub. GitHub blob and tree URLs are normalized to raw template content by the CLI before the project is created. 
cloudeval projects create \
  --template-url https://github.com/Azure/azure-quickstart-templates/blob/master/quickstarts/microsoft.compute/1vm-2nics-2subnets-1vnet/azuredeploy.json \
  --name "Nightly evaluation" \
  --provider azure \
  --profile nightly \
  --format json \
  --output ./cloudeval-project.json

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Capture project and connection IDs

The response includes the created project and connection objects. Persist the returned IDs instead of guessing or reconstructing them later: 
PROJECT_ID=$(jq -r '.data.project.id' ./cloudeval-project.json)
CONNECTION_ID=$(jq -r '.data.connection.id' ./cloudeval-project.json)

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

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Compile Bicep, then import

CloudEval’s strongest IaC path today is ARM JSON. If your source of truth is Bicep, compile it first: 
az bicep build --file ./main.bicep --outfile ./dist/template.json

cloudeval projects create \
  --template-file ./dist/template.json \
  --name "Bicep import" \
  --provider azure \
  --profile nightly \
  --format json \
  --output ./cloudeval-project.json

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Run a full evaluation

cloudeval reports run \
  --project "$PROJECT_ID" \
  --type all \
  --wait \
  --profile nightly \
  --format json
Current report run types are:
  • cost
  • waf
  • architecture
  • unit-tests
  • all
Use --no-save-report when you want a transient run instead of saving the report snapshot.

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Download report payloads for another system

Download all available saved report payloads for a project: 
cloudeval reports download \
  --project "$PROJECT_ID" \
  --type all \
  --view parsed \
  --output ./reports \
  --profile nightly \
  --format json \
  --non-interactive
Notes:
  • --type all writes multiple files when the output target is a directory.
  • --view supports raw, parsed, and formatted.
  • --type architecture currently maps to the architecture or WAF-style payload for that project.

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Ask a grounded question in a script

cloudeval ask "List the highest severity architecture findings for this project" \
  --project "$PROJECT_ID" \
  --profile nightly \
  --format json \
  --non-interactive \
  --print-url \
  --no-open
This is the right pattern when you want a single grounded answer plus a CloudEval app link for follow-up review. Review the local session produced by ask: 
cloudeval sessions list --profile nightly --format json
cloudeval sessions search "architecture findings" --profile nightly --format json
cloudeval sessions get <thread-id> --profile nightly --format json
Give an important session a readable title and continue it later: 
cloudeval sessions rename <thread-id> "Nightly architecture review" --profile nightly --format json
cloudeval chat --resume "Nightly architecture review" --profile nightly
Reuse a known thread ID when a scheduled job should keep context together: 
cloudeval ask "Compare this run with the previous review" \
  --thread <thread-id> \
  --project "$PROJECT_ID" \
  --profile nightly \
  --format json \
  --non-interactive

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Manage model defaults

Use models list to see backend-supported models, then set a profile default: 
cloudeval models list --profile nightly --format json
cloudeval models default set gpt-5-nano --profile nightly --format json
Commands such as ask, chat, and tui use that profile model when --model is not passed.

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Run CloudEval as an MCP server

Use this when your automation platform already supports MCP and should call CloudEval tools directly. 
cloudeval mcp status --format json
cloudeval doctor --mcp --format json
codex mcp add cloudeval -- cloudeval mcp serve
Generate client setup instructions: 
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 generic when your MCP client, including an Ollama-powered agent host, accepts an mcpServers JSON entry. Choose a focused toolset for tighter agent access: 
cloudeval mcp serve --toolset readonly
cloudeval mcp serve --toolset reports
cloudeval mcp serve --toolset billing
Important notes:
  • mcp serve uses stdio
  • auth can come from stored cloudeval login, stored cloudeval login --headless, or --machine
  • run login before starting mcp serve; stdin is reserved for MCP messages
  • clients that support MCP resources and prompts can discover CloudEval capabilities, project context, billing summaries, latest reports, and review prompt templates
Open commands are useful when an automation should hand a human the next page to inspect. Project diagram: 
cloudeval open project "$PROJECT_ID" \
  --view both \
  --layout dependency \
  --profile nightly \
  --print-url \
  --no-open
Headless diagram bytes: 
export CLOUDEVAL_BASE_URL="${CLOUDEVAL_BASE_URL:-https://cloudeval.ai}"

mkdir -p diagram-downloads

cloudeval projects export-diagram "$PROJECT_ID" \
  --layout dependency \
  --format png \
  --labels all \
  --output diagram-downloads/dependency.png \
  --headers-output diagram-downloads/dependency.headers \
  --base-url "$CLOUDEVAL_BASE_URL" \
  --non-interactive
Use Headless diagram image downloads for architecture/dependency, PNG/JPEG/SVG, public/share, and security-check variants. Reports page: 
cloudeval open reports \
  --project "$PROJECT_ID" \
  --report-type cost \
  --profile nightly \
  --print-url \
  --no-open
Billing usage page: 
cloudeval open billing \
  --tab usage \
  --profile nightly \
  --print-url \
  --no-open

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Where the web app still matters

The CLI does not replace every browser workflow.
  • Visual diagram inspection is still better in the web app.
  • Some connection setup flows remain web-app oriented.
  • Sharing and collaboration are easier to manage in the browser.

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Next step

Use CLI command reference for the full command surface, or Agent and automation rules if you are building a more structured integration.
Last modified on May 8, 2026