Training course · Model Context Protocol (MCP) · Software Architecture

Model Context Protocol (MCP) for Software Architects

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 Software Architects” is designed for roles such as Software architects and solution architects, Enterprise architects with links to application development and Senior developers and tech leads with architectural responsibility and shows step by step how to use Model Context Protocol (MCP) productively, safely and transparently in Software Architecture – 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.

When AI agents write large parts of the code, architecture becomes more important, not less: clear module boundaries, documented decisions and verifiable quality goals are the frame that keeps generated code consistent. AI assistants also support architects directly, for example by analysing existing codebases, producing C4 diagrams, drafting architecture decision records and weighing alternatives against quality attributes. Fitness functions and architecture tests can be generated and anchored in your pipelines. In this course you learn to use AI as a sparring partner for architectural decisions, prepare architectural knowledge so that coding agents follow it reliably and spot erosion early in fast-growing codebases.

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?

  • Software architects and solution architects
  • Enterprise architects with links to application development
  • Senior developers and tech leads with architectural responsibility
  • Platform and cloud architects
  • Owners of architecture governance and review boards

Typical challenges we address

  • Architecture documentation goes stale quickly and drifts from the actual code
  • Decisions are made verbally and their context is lost
  • Gradual architectural erosion through cyclic dependencies and violated layer boundaries
  • Without clear guidance, AI-generated code ignores established patterns and conventions

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 to analyse existing systems, evaluate architectural options and record decisions as ADRs.
  • Anchor architecture rules as fitness functions in the pipeline and detect erosion early.
  • Shape context and rules so that AI coding agents reliably respect your architectural guidelines.

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: Software Architecture
  • Documenting architecture decisions
  • Reconstructing existing architecture
  • Evaluating alternatives
  • Generating fitness functions
  • Guardrails for coding agents
  • Carving out domains
Module 7 Data protection, quality & transfer to daily work
  • Architecture documents often describe security-relevant details such as network zones and data flows: you learn which information may be shared with AI services, how to assess licensing risks of components and how architectural decisions traceably reflect GDPR, NIS2 and Cyber Resilience Act requirements.
  • 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

Documenting architecture decisions

Turn discussions, meeting notes and pull requests into structured architecture decision records with context, options considered, decision and consequences, stored consistently in the repository. Related decisions are linked automatically.

Reconstructing existing architecture

Analyse existing codebases, extract modules, dependencies and interfaces and generate up-to-date C4 diagrams as diagrams-as-code with PlantUML, Mermaid or Structurizr. Diagrams can be refreshed automatically on every build.

Evaluating alternatives

Compare options such as a modular monolith, microservices or event-driven integration against quality scenarios, cost and team structure, and make trade-offs visible following ATAM principles.

Generating fitness functions

Express architecture rules such as layer access, forbidden dependencies or naming conventions as automated tests with ArchUnit, dependency-cruiser or similar tools and wire them into CI.

Guardrails for coding agents

Describe architectural principles, module boundaries and patterns in rule files and context documents so AI agents follow them reliably when generating code, and spot deviations in review.

Carving out domains

Derive bounded contexts, aggregates and context maps following domain-driven design from event storming results and business descriptions, and use them to shape teams and services.

Possible hands-on projects

ADR assistant

A command-line tool that turns bullet points into a complete ADR from a template, links related decisions and updates an index in the repository.

Dependency visualiser

A script that analyses import relationships in a codebase, flags cycles and layer violations and renders an interactive diagram in the browser.

Architecture Q&A over repos

A small application that indexes ADRs, diagrams and code and answers architecture questions with references to the underlying sources. New team members onboard faster.

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. You have several years of development experience and have made or supported architectural decisions in larger software systems.

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

Architecture documents often describe security-relevant details such as network zones and data flows: you learn which information may be shared with AI services, how to assess licensing risks of components and how architectural decisions traceably reflect GDPR, NIS2 and Cyber Resilience Act requirements.

Suggested dates

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

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Thu, 3 December 2026 – Fri, 4 December 2026 Munich Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Online Request
  • Tue, 15 December 2026 – Wed, 16 December 2026 Stuttgart Request
  • Wed, 13 January 2027 – Thu, 14 January 2027 Hanover Request
  • Thu, 28 January 2027 – Fri, 29 January 2027 Online 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.

Can AI make architecture decisions?

AI is a strong sparring partner: it offers options, names trade-offs, finds gaps in reasoning and documents decisions in a structured way. Responsibility stays with people, because architecture depends on business context, team skills, budget and risk appetite. In the course you learn to use AI so that decisions are better reasoned and made faster.

How do we stop AI agents from eroding the architecture?

Through a combination of clear context and automated checks: rule files and ADRs describe module boundaries and patterns, fitness functions and architecture tests in CI block violations, and reviews focus on structural changes. Architecture thus turns from documentation into a verifiable guardrail for humans and agents alike.

How much does “Model Context Protocol (MCP) for Software Architects” 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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