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Use CloudEval’s MCP server when an agent should call CloudEval as a tool server instead of parsing CLI text output.
CloudEval command line and agent panel showing actions for ChatGPT, Claude, Codex, Cursor, and VS Code

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Before you configure a client

Check the local MCP surface first: 
cloudeval mcp status --format json
cloudeval doctor --mcp --format json
Authenticate with one of these options:
  • stored cloudeval login credentials
  • stored cloudeval login --headless credentials for remote or SSH sessions
  • --machine when your environment supports machine access
mcp serve uses stdin for MCP protocol messages, so run login before starting the MCP server.

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Choose a client

CloudEval MCP setup examples for Codex, Cursor, Claude Code, and VS Code using cloudeval mcp serve
Client-specific steps also have stable in-page anchors for deeplinks from the app (#codex, #cursor, #claude-code, #vs-code).

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Codex

Generate the Codex setup guidance: 
cloudeval mcp setup codex --dry-run
A typical local registration uses the CloudEval stdio server: 
codex mcp add cloudeval -- cloudeval mcp serve
Use a focused toolset when Codex should only inspect part of the product: 
codex mcp add cloudeval-reports -- cloudeval mcp serve --toolset reports

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Cursor

Generate Cursor setup guidance: 
cloudeval mcp setup cursor --dry-run
Use the generated mcpServers entry in Cursor’s MCP configuration surface. Keep secrets in Cursor environment configuration or stored CloudEval login state, not inline command arguments.

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Claude Code

If your Claude Code environment accepts an mcpServers JSON entry, start with the generic output: 
cloudeval mcp setup generic --dry-run --format json
If your local CloudEval CLI supports a Claude-specific target for your installed Claude client, you can inspect that output too: 
cloudeval mcp setup claude --dry-run
Use the generated stdio command and keep authentication in stored login state or environment variables.

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VS Code

Generate VS Code MCP configuration for the current workspace: 
cloudeval mcp setup vscode --dry-run
By default this targets .vscode/mcp.json in the current directory. Use the generated stdio command in your VS Code MCP extension or built-in MCP configuration surface. If your extension expects generic mcpServers JSON instead, you can still use: 
cloudeval mcp setup generic --dry-run --format json
The important part is that the server command runs: 
cloudeval mcp serve
mcp setup --toolset supports all, readonly, projects, reports, and billing. Use mcp serve --toolset graph or mcp serve --toolset validation when the editor agent needs those narrower surfaces.

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Focus tool access

Use focused toolsets when the assistant should have a narrower CloudEval surface.
ToolsetUse it for
allFull CloudEval MCP surface
readonlyRead-only project, report, billing, and capability discovery
projectsProject lookup, creation handoff, project deeplinks, and project context
reportsReport runs, report downloads, report deeplinks, and latest report context
billingBilling and credit inspection
graphProject graph, timeline, diff, sync runs, and insight reads
validationTemplate validation, parsing, and validation rule catalog reads
Example: 
cloudeval mcp serve --toolset readonly
cloudeval mcp serve --toolset graph
cloudeval mcp serve --toolset validation
Agent Profile tools are available in the full and read-only MCP surfaces: agent_profiles_list, agent_profiles_get, and agent_profiles_run. They use the same canonical profile ids as the CLI: architecture, cost, triage, and remediation. The validation toolset exposes template_validate, template_test, template_parse, rules_categories, rules_search, and rules_get. template_validate, template_test, and template_parse all accept an optional parametersFile argument. template_validate accepts ruleId for one check or ruleNames for multiple check ids, details for frontend-style per-check evidence, and wait when the client needs completed validation results instead of only a submitted job. template_test accepts includeTests, skipTests, category, testGroups, and wait. Use waitTimeoutMs with wait for bounded automation runs. When a validation call uses wait, MCP clients can include _meta.progressToken in the tools/call params to receive notifications/progress updates for queued, running, and completed states. Completed progress messages include failing check/test details such as message, recommendation, severity, and file/template or resource location when available. If a completed backend result only has a worker-local temp file path, CloudEval reports the submitted template filename instead. The graph toolset exposes projects_graph_get, projects_graph_timeline, projects_graph_diff, projects_graph_insights, and projects_graph_sync_runs.

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Verify the integration

After configuring the client:
  1. Restart the MCP client if it requires a restart.
  2. Ask the client to list available CloudEval tools.
  3. Run a low-risk command such as capability discovery or project listing.
  4. Confirm the result uses the expected CloudEval profile, base URL, and auth mode.
CloudEval agent result explaining selected Azure resources with diagram context and generated Mermaid output

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Next step

Use Agent and automation rules for safety defaults, or CLI command reference for the MCP command surface.
Last modified on June 22, 2026