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Manage Partiri Cloud workspaces, projects, services, deployments, and metrics with AI.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Partiri Cloud MCP Server" yet — see the docs or source repo.
List all workspaces in Partiri Cloud and their projects, then summarize current service status by project.
A structured list of workspaces, projects, and service statuses for quick inspection.
Check the latest deployment status for a specific service and tell me whether it succeeded and whether any issue needs attention.
The latest deployment result, a status assessment, and any issues that need attention.
Fetch recent metrics for this service and summarize any notable spikes or abnormal trends.
A metrics overview with brief analysis to help assess service health.
DevOps engineers or developers can use AI to review workspaces, projects, and services in Partiri Cloud from one place. It is useful for routine checks and environment inventory.
After a release, teams can ask AI to check deployment status and summarize the result to confirm whether the rollout succeeded. This fits post-release checks and initial troubleshooting.
When service performance needs review, this tool can fetch metrics and let AI summarize them. It is suitable for initial observation and reporting of unusual changes.
It is an MCP server for the Partiri Cloud PaaS platform that lets AI agents manage workspaces, projects, services, deployments, and metrics.
Based on the provided description, it supports management related to workspaces, projects, services, deployments, and metrics. See the source repository for more detailed operation scope.
The provided material does not include installation steps, runtime requirements, or credential details. See the source repository for exact prerequisites.
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