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Version: v1.0.x

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 — follow the Configuring MCP Servers with AI Assistants guide
  2. Review available tools — 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 →


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 →


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 — logs, distributed traces, and deployment inspection — 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 →


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 →


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 →