Training course · Model Context Protocol (MCP) · Kubernetes & Microservices

Model Context Protocol (MCP) for Kubernetes and Microservices 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

This course connects Model Context Protocol (MCP) with the everyday processes of Kubernetes & Microservices. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

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.

Kubernetes has become the standard platform for microservices – and with it comes a flood of YAML manifests, Helm charts, Kustomize overlays, operators, service mesh configuration and GitOps repositories. On top of that, failures are distributed across dozens of services. AI coding assistants and agents take a lot of that complexity off your hands: they generate Helm charts with sensible defaults, diagnose CrashLoopBackOff and OOMKilled errors from events and logs, write NetworkPolicies and RBAC rules on the least-privilege principle and help define service boundaries. At the same time, an agent with cluster access needs firm limits. This course teaches you to use AI for manifests, troubleshooting and architecture work without losing control of your clusters.

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?

  • Platform engineers and Kubernetes administrators
  • Developers running microservices on Kubernetes
  • Site reliability engineers with cluster responsibility
  • Software architects designing distributed systems
  • DevOps teams introducing or scaling GitOps

Typical challenges we address

  • Unwieldy amounts of YAML, Helm values and overlays across many environments
  • Difficult troubleshooting of distributed issues spanning services, networking and resource limits
  • Security gaps from overly broad RBAC, missing NetworkPolicies and privileged containers
  • Unclear service boundaries and tight coupling in growing microservice landscapes

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.
  • Create and review Helm charts, overlays and policies efficiently with AI agents.
  • Diagnose issues in clusters and microservices faster with structured AI support.
  • Restrict agents’ cluster access and enforce security and architecture rules consistently.

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: Kubernetes & Microservices
  • Creating Helm charts and Kustomize overlays
  • Debugging pods and deployments
  • RBAC and NetworkPolicies
  • Service boundaries and communication
  • Setting up observability
  • Maintaining GitOps repositories
Module 7 Data protection, quality & transfer to daily work
  • Kubeconfigs, service account tokens and secrets must never end up in prompts, and agents should only work with read-only or tightly scoped rights in non-production clusters. Check container images and charts for provenance, signatures and vulnerabilities, and take into account the supply-chain and SBOM requirements of NIS2 and the Cyber Resilience Act.
  • 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

Creating Helm charts and Kustomize overlays

The agent generates Helm charts or Kustomize overlays with probes, resource requests, pod disruption budgets and security contexts, validates them with helm lint and kubeconform and documents every value.

Debugging pods and deployments

From kubectl output, events and container logs, the AI narrows down causes of CrashLoopBackOff, OOMKilled, ImagePullBackOff or failing readiness probes and proposes concrete fixes. The team reviews every proposed fix before applying it.

RBAC and NetworkPolicies

The AI analyses existing roles and traffic flows, writes least-privilege RBAC rules and creates NetworkPolicies that allow only the traffic services genuinely need. Changes are tested in a non-production cluster first.

Service boundaries and communication

The assistant helps define service boundaries along business domains, weigh synchronous against asynchronous communication and implement patterns such as outbox, circuit breaker or saga properly.

Setting up observability

Services get OpenTelemetry instrumentation, Prometheus metrics, ServiceMonitors and alert rules, plus dashboards that make latency, error rate and saturation visible. Alerts are aligned with service level objectives so that only relevant incidents escalate.

Maintaining GitOps repositories

The AI structures repositories for Argo CD or Flux, separates environments cleanly, automates image updates and explains sync failures using status output and diffs. Drift between repository and cluster becomes visible.

Possible hands-on projects

Manifest linter with a rule set

A tool that checks Kubernetes manifests for missing limits, probes, labels and insecure settings and produces a report for pull requests.

Microservice demo with tracing

Two or three small services instrumented with OpenTelemetry, deployed via Helm to a local cluster, including a dashboard for distributed traces.

Cluster hygiene report

A CLI that finds unused ConfigMaps, orphaned PersistentVolumes and deployments without resource settings and suggests clean-up actions. Nothing is deleted without human approval.

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 containers, kubectl and core Kubernetes objects, plus a working knowledge of Helm, YAML and Git.

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

Kubeconfigs, service account tokens and secrets must never end up in prompts, and agents should only work with read-only or tightly scoped rights in non-production clusters. Check container images and charts for provenance, signatures and vulnerabilities, and take into account the supply-chain and SBOM requirements of NIS2 and the Cyber Resilience Act.

Suggested dates

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

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Salzburg Request
  • Thu, 17 December 2026 – Fri, 18 December 2026 Online Request
  • Wed, 13 January 2027 – Thu, 14 January 2027 Bremen Request
  • Thu, 28 January 2027 – Fri, 29 January 2027 Online Request
  • Thu, 4 February 2027 – Fri, 5 February 2027 Augsburg 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 have direct access to my cluster?

Only in a controlled way. For analysis and troubleshooting, read-only rights in selected namespaces are usually enough, while changes should go through GitOps repositories and reviews. On the course you set up dedicated service accounts, local test clusters and approval processes so the agent is useful without creating production risk.

Does the course also cover microservice architecture questions?

Yes. Beyond manifests and operations, we look at how AI can support defining service boundaries, weighing communication patterns and assessing architecture options. The AI provides options and arguments, while the decision stays with the team and is documented transparently. That keeps decisions accountable.

How much does “Model Context Protocol (MCP) for Kubernetes and Microservices 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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