sudosmitty/dynatrace-ai-mcp-workshop
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Collaborators are not being added, and the workshop token is not being rotated to avoid impacting active workshops. Please contact the internal team to obtain the current workshop token.
概要
Collaborators are not being added, and the workshop token is not being rotated to avoid impacting active workshops. Please contact the internal team to obtain the current workshop token. A hands-on workshop for learning AI/LLM observability with Dynatrace and the Model Context Protocol (MCP). The guide walks you through launching your Codespace, configuring your environment, and completing all labs with detailed instructions, code snippets, and screenshots. - ✅ Instrument AI/LLM applications with OpenLLMetry - ✅ Send traces to Dynatrace via OTLP - ✅ Analyze LLM performance, token usage, and costs - ✅ Use Dynatrace MCP for agentic AI workflows - ✅ Automate AI cost monitoring with Dynatrace Workflows —they don't fork it. Each attendee launches their own isolated Codespace, and all their code modifications stay private to that Codespace session. The workshop uses a pre-deployed to securely distribute Azure OpenAI credentials.
README
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🎯 Dynatrace AI Observability & MCP Workshop
[!IMPORTANT]
Collaborators are not being added, and the workshop token is not being rotated to avoid impacting active workshops. Please contact the internal team to obtain the current workshop token.
A hands-on workshop for learning AI/LLM observability with Dynatrace and the Model Context Protocol (MCP).
🚀 Workshop Attendees: Start Here!
The guide walks you through launching your Codespace, configuring your environment, and completing all labs with detailed instructions, code snippets, and screenshots.
📋 Workshop Overview
| Duration | 2 - 2.5 hours |
|---|---|
| Level | Intermediate |
| Format | Hands-on Labs |
| Platform | GitHub Codespaces |
What You’ll Learn
- ✅ Instrument AI/LLM applications with OpenLLMetry
- ✅ Send traces to Dynatrace via OTLP
- ✅ Analyze LLM performance, token usage, and costs
- ✅ Use Dynatrace MCP for agentic AI workflows
- ✅ Automate AI cost monitoring with Dynatrace Workflows
📚 Workshop Labs
| Lab | Duration | Description |
|---|---|---|
| Lab 0: Setup | 15 min | Environment configuration |
| Lab 1: Instrumentation | 15 min | Add OpenLLMetry to the sample app |
| Lab 2: Explore Traces | 30 min | Analyze AI traces in Dynatrace |
| Lab 3: Dynatrace MCP | 30 min | Use MCP for agentic AI |
| Lab 4: Automation | 30 min | Create automated workflows |
👨🏫 Instructor Setup
How It Works
Attendees share the same repository—they don’t fork it. Each attendee launches their own isolated Codespace, and all their code modifications stay private to that Codespace session.
The workshop uses a pre-deployed Azure Function secrets server to securely distribute Azure OpenAI credentials. Attendees enter a workshop token—they never see the actual API keys.
🛤️ Choose Your Setup Path
There are two ways to run this workshop as an instructor:
| Path | Best For | Requirements |
|---|---|---|
| Option A: Use Existing Infrastructure | Most instructors | Collaborator access to this repository |
| Option B: Stand Up Your Own | Using your own Dynatrace tenant + Azure OpenAI | Azure subscription + deployment skills |
Option A: Use Existing Infrastructure (Recommended)
インストール
This server does not publish a one-line install command.
Open the repository installation guide MCP 初心者？mcpskill.org のガイドから始めましょう →