Skip to main content
Version: v1.0.0-rc.2 (pre-release)

MCP Prompt Scenarios

This page provides a collection of practical prompt scenarios for using the OpenChoreo MCP server with AI assistants. Each scenario is a hands-on guide that walks you through real-world tasks using natural language prompts.

Prerequisites​

Before trying these scenarios:

  1. Configure your MCP server β€” follow the Configuring MCP Servers with AI Assistants guide
  2. Review available tools β€” see the MCP Servers Reference for the full list of MCP tools

Scenarios​

1. Getting Started​

Learn the basics of connecting your AI assistant to OpenChoreo and performing simple operations like listing namespaces and projects.

Time: ~2 minutes

View guide on GitHub β†’


2. Service Deployment​

Deploy a complete service from source code to production using the OpenChoreo MCP server. Choose between a step-by-step guided walkthrough or a natural conversation-based deployment.

Time: ~15-20 minutes

View guide on GitHub β†’


3. Log Analysis & Debugging​

Debug a cascading failure in the GCP Microservices Demo (Online Boutique) application. You'll intentionally break the product catalog service by scaling it to zero replicas, then use AI-assisted observability β€” logs, distributed traces, and deployment inspection β€” to diagnose and fix the issue across service boundaries.

Key MCP tools: list_components, query_component_logs, query_traces, query_trace_spans, get_release_binding, update_release_binding

Time: ~10 minutes

View guide on GitHub β†’


4. Build Failure Diagnosis​

Debug a Docker build failure in the Go Greeter service. You'll trigger a build with a misconfigured Dockerfile path, then use AI-assisted workflow inspection and log analysis to diagnose the root cause, compare with the previous successful build, and apply the fix.

Key MCP tools: list_workflow_runs, get_workflow_run, query_workflow_logs, create_workflow_run

Time: ~10 minutes

View guide on GitHub β†’


5. Resource Optimization​

Detect and fix over-provisioned deployments in the GCP Microservices Demo (Online Boutique). You'll intentionally allocate excessive CPU and memory to several services, then use AI-assisted analysis to compare allocation vs actual usage, get right-sizing recommendations, and apply optimized configurations.

Key MCP tools: list_components, list_release_bindings, get_release_binding, query_resource_metrics, update_release_binding

Time: ~10 minutes

View guide on GitHub β†’