Training course · Model Context Protocol (MCP) · DevOps & Cloud

Model Context Protocol (MCP) for DevOps and Cloud Teams

Build and connect MCP servers securely: tools, resources and prompts for Claude Code, Cursor, Copilot and your own AI agents.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Model Context Protocol (MCP) for DevOps and Cloud Teams” is designed for roles such as DevOps engineers and platform teams, Cloud architects working with AWS, Azure or Google Cloud and Site reliability engineers responsible for operations and incidents and shows step by step how to use Model Context Protocol (MCP) productively, safely and transparently in DevOps & Cloud – from the fundamentals to advanced workflows.

The Model Context Protocol is an open standard that gives AI applications a uniform way to access external tools, data sources and workflows. Instead of building separate integrations for every assistant, you implement an MCP server once and use it in Claude Code, Claude Desktop, Cursor, GitHub Copilot, VS Code and your own agents. In this two-day course you learn the host, client and server architecture, the three core primitives of tools, resources and prompts, and the stdio and Streamable HTTP transports, applying each of them directly in your own code.

On day one you use the official TypeScript or Python SDK to build an MCP server that exposes internal APIs, databases or files to AI assistants in a controlled way, and you test it with the MCP Inspector. Day two covers remote servers, OAuth-based authorisation, security risks such as prompt injection and tool poisoning, and operations, logging and governance in an enterprise setting. You learn to write tool descriptions that models use reliably, and you leave knowing how to integrate MCP servers safely into your development and agent landscape.

DevOps and cloud teams own infrastructure as code, CI/CD pipelines, monitoring, cost control and security – often across several cloud providers and on-premises environments. Much of the work is configuration in HCL, YAML and shell, troubleshooting through logs and cross-checking documentation. AI coding assistants and agents speed up exactly that: they write reusable Terraform modules, fix failing pipelines, create Ansible roles, analyse incident logs and propose cost optimisations. At the same time the stakes are high, because a single wrong command can hit production. This course teaches you to use agents productively with clear permission boundaries, plan-and-review workflows and safeguarded environments.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • DevOps engineers and platform teams
  • Cloud architects working with AWS, Azure or Google Cloud
  • Site reliability engineers responsible for operations and incidents
  • System administrators moving towards infrastructure as code
  • Developers who maintain their own CI/CD pipelines

Typical challenges we address

  • Growing volumes of Terraform, YAML and pipeline configuration with drift and duplication
  • Time-consuming troubleshooting of failing pipelines and distributed logs
  • Rising cloud costs without clear visibility
  • High demands on security, compliance and traceability of changes

What you will be able to do

  • You explain the MCP architecture and choose between tools, resources and prompts for each use case.
  • You develop your own MCP server in TypeScript or Python and test it systematically.
  • You connect MCP servers to Claude Code, Cursor, Copilot and custom agents.
  • You run remote servers using Streamable HTTP and OAuth-based authorisation.
  • Use AI agents safely for infrastructure as code, pipelines and configuration management.
  • Analyse incidents, pipeline failures and cloud costs faster with AI support.
  • Define permission boundaries and approval processes so agents cannot make uncontrolled changes to production.

Course outline

Day 1

Module 1 Architecture and core concepts
  • Roles of host, client and server and the lifecycle of a connection
  • JSON-RPC messages, capability negotiation and notifications
  • Tools, resources and prompts: choosing the right primitive
  • Client features such as sampling, roots and elicitation at a glance
Module 2 Building your own MCP server
  • Setting up a server with the official TypeScript or Python SDK
  • Defining tools cleanly with JSON Schema for inputs and outputs
  • Exposing resources and resource templates for documents, records and configuration
  • Testing and debugging with the MCP Inspector and meaningful error messages
Module 3 Connecting to Claude Code, Cursor, Copilot and agents
  • Configuring local stdio servers and remote servers in clients
  • Project-level and user-level configuration, approvals and permissions
  • Writing tool descriptions that models reliably select correctly
  • Integrating MCP servers into custom agents built with the Claude Agent SDK or OpenAI Agents SDK
Module 4 Transport, authorisation and operations
  • Streamable HTTP, session management and stateless servers
  • OAuth-based authorisation, token validation and integration with existing identity providers
  • Deploying as containers, on serverless platforms or behind a gateway
  • Logging, monitoring, rate limiting and versioning tool interfaces

Day 2

Module 5 Security and governance
  • Threats: prompt injection via tool results, tool poisoning, confused deputy
  • Least privilege, confirmation requirements and sandboxing
  • Reviewing and approving third-party servers within the organisation
  • Data protection, logging of personal data and requirements under GDPR and the EU AI Act
Module 6 Practice lab: DevOps & Cloud
  • Developing Terraform modules
  • Fixing CI/CD pipelines
  • Ansible and configuration management
  • Incident analysis
  • Optimising cloud costs
  • Policy as code and hardening
Module 7 Data protection, quality & transfer to daily work
  • Agents with access to cloud accounts and pipelines are a security matter: work with least privilege, separate environments and human approval, and keep cloud credentials, state files and secrets out of prompts. Operators of essential and important services must also meet NIS2 requirements on risk management, supply-chain security and traceability of changes.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Developing Terraform modules

The agent creates reusable Terraform or OpenTofu modules with variables, validations and outputs, runs fmt, validate and plan, and explains every planned change before a human approves the apply.

Fixing CI/CD pipelines

Failed GitHub Actions, GitLab CI or Azure DevOps runs are analysed from their logs, root causes such as caching issues or missing permissions identified and corrected pipeline definitions proposed.

Ansible and configuration management

The AI writes idempotent Ansible roles, templates and inventories, replaces fragile shell scripts and adds Molecule tests so configuration stays reproducible. Existing roles are gradually standardised along the way.

Incident analysis

Logs, metrics and alerts from monitoring are brought together, a timeline reconstructed, likely causes narrowed down and a draft postmortem with actions produced. The team keeps the final judgement.

Optimising cloud costs

The assistant reviews cost reports and resource definitions, finds oversized instances, orphaned volumes or missing lifecycle rules and drafts the matching IaC changes. Each recommendation includes estimated savings.

Policy as code and hardening

IaC code is scanned with tools such as Checkov or tfsec, findings are fixed and rules for encryption, tagging or network access are expressed as policy as code.

Possible hands-on projects

Drift detector

A script that regularly runs terraform plan across several environments, detects drift and posts a summary to a chat channel.

Pipeline template library

Reusable CI/CD templates for build, test, security scanning and deployment, documented and accompanied by sample projects. Teams adopt them with just a few lines of configuration.

Cost report tool

A CLI that pulls cost data from a cloud account, breaks it down by tags and teams and clearly highlights optimisation candidates.

Prerequisites

Solid programming skills in TypeScript/JavaScript or Python and a basic understanding of HTTP APIs and JSON. Some experience using AI assistants in day-to-day development is helpful. Practical experience with Linux, Git and at least one cloud platform, plus a working knowledge of Terraform, containers and CI/CD.

Technical requirements

A laptop with Node.js or Python, Git and a code editor, plus access to at least one MCP-capable client such as Claude Code, Claude Desktop, Cursor or VS Code with Copilot.

Data protection & compliance

Agents with access to cloud accounts and pipelines are a security matter: work with least privilege, separate environments and human approval, and keep cloud credentials, state files and secrets out of prompts. Operators of essential and important services must also meet NIS2 requirements on risk management, supply-chain security and traceability of changes.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Stuttgart Request
  • Tue, 12 January 2027 – Wed, 13 January 2027 Online Request
  • Mon, 18 January 2027 – Tue, 19 January 2027 Berlin Request
  • Tue, 26 January 2027 – Wed, 27 January 2027 Online Request
  • Thu, 11 February 2027 – Fri, 12 February 2027 Munich Request

Frequently asked questions

Why would I need my own MCP server?

Whenever AI assistants need access to internal systems such as ticketing tools, databases, documentation or line-of-business applications. Your own server defines precisely which actions and data are available and can then be reused across several clients. That avoids isolated one-off integrations and keeps access firmly under your control.

Which programming language does the course use?

You can choose between TypeScript and Python, both of which have official SDKs. The concepts are identical and all exercises are available in both versions. Developers working in other languages still benefit, because the protocol is language-agnostic and SDKs for other languages follow a similar structure.

How secure is MCP for enterprise use?

The protocol provides mechanisms for authorisation and permissions, but real-world security depends on implementation and operations. Risks such as prompt injection through tool results or over-privileged servers are genuine. We cover concrete countermeasures, approval processes for third-party servers and how to handle personal data.

Should an AI agent be allowed to apply infrastructure changes itself?

That is something to limit deliberately. A proven workflow has the agent write code and run plan, a human review the result and the apply run through the regular pipeline. On the course you learn to design permissions, sandbox environments and approvals so that production systems stay protected.

Which cloud platform does the course use?

The methods are vendor-neutral and transfer to AWS, Azure and Google Cloud. Exercises use examples you can reproduce with local tooling or test accounts. You are welcome to bring scenarios from your own platform, provided no production credentials are used.

How much does “Model Context Protocol (MCP) for DevOps and Cloud Teams” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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