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Manage AI workers via MCP with versioning, safe testing, deployment, approvals, and policy-based control.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "AgentCloud MCP Server" yet — see the docs or source repo.
Use AgentCloud MCP Server to create a new version of the AI worker named report-worker, run safe testing, and do not deploy it if the test fails.
Return the new worker version details and test results, and indicate whether it can proceed to deployment.
Check the latest version of the AI worker named data-worker, request approval, deploy it only after approval, and then report its run lifecycle state.
Return approval status, deployment result, and whether it has entered a runnable lifecycle stage.
Review run requests for the same worker across tenant A and tenant B, apply policy rules, and block non-compliant cross-tenant execution.
Output policy decisions per tenant and clearly list any requests blocked by policy.
Development teams iterating AI worker features can use versioning plus test-before-deploy flow to reduce release risk and keep rollback capability.
When multiple tenants share the same platform, the tool enforces tenant isolation and policy during the run lifecycle to prevent cross-tenant interference.
Ops and security teams can require approval and testing before release so AI worker operations stay controlled and policy-compliant.
It is an MCP tool for managing persistent AI workers, including versioning, testing, deployment, approvals, run lifecycle management, tenant isolation, and policy enforcement.
Only the MCP nature of the tool is provided here, with no specific setup details, keys, or runtime requirements. Check the source repository for exact integration instructions.
It focuses on the full AI worker lifecycle—versioning, testing, approval, deployment, and run governance—with tenant isolation and policy enforcement; for detailed comparisons with similar tools, see the source repository.
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