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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

CloudEval can hand the current project context to AI clients and copy setup prompts for Codex, Cursor, Claude, and VS Code.

Video walkthrough: make cloud evaluations available to agents, automation workflows, and local developer tools.

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Before you configure a client

Check the local MCP surface first:
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

Codex uses a registration command; Cursor, Claude Code, VS Code, and generic MCP clients use the same stdio server configuration shape.

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:
A typical local registration uses the CloudEval stdio server:
Use a focused toolset when Codex should only inspect part of the product:

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Cursor

Generate Cursor setup guidance:
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:
If your local CloudEval CLI supports a Claude-specific target for your installed Claude client, you can inspect that output too:
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:
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:
The important part is that the server command runs:
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. Example:
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

A useful agent result should stay grounded in CloudEval project context, selected resources, and the visible diagram or report state.

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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