Training course · Model Context Protocol (MCP) · AI Engineering & LLM Applications

Model Context Protocol (MCP) for AI Engineers

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 AI Engineering & LLM Applications. 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.

AI engineering is the discipline of turning language models into dependable products: chat assistants, document analysis, retrieval-augmented generation, agents with tool access and AI features inside existing applications. The code around the model is often more demanding than the model call itself, because prompt versioning, structured output, evaluation, cost control, latency, observability and protection against prompt injection decide success in production. AI coding assistants and agents accelerate exactly this work, from building evaluation pipelines and tool definitions to MCP servers and root-cause analysis in traces. In this course you learn to develop LLM applications systematically with AI support, make their quality measurable and run them securely, cost-effectively and in compliance with regulation.

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 developers integrating LLM features into products
  • AI engineers and machine learning engineers
  • Data scientists focusing on generative AI
  • Backend and platform teams providing AI infrastructure
  • Technical product owners for AI features

Typical challenges we address

  • Prototypes shine in demos but deliver inconsistent quality in production
  • Without systematic evaluation, changes to prompts and models are risky
  • Costs, latency and rate limits spiral as usage grows
  • New security risks from prompt injection, data leakage and over-privileged agents

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.
  • Build RAG applications, agents and LLM features with AI coding tools systematically and to production standard.
  • Make the quality of LLM applications measurable and manageable with automated evaluations.
  • Run LLM applications securely and cost-effectively, with observability, prompt injection defences and clear permissions.

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: AI Engineering & LLM Applications
  • Building RAG pipelines
  • Automating evaluation
  • Developing agents and tools
  • Securing structured output
  • Observability and cost
  • Defending against prompt injection
Module 7 Data protection, quality & transfer to daily work
  • LLM applications often process personal and confidential data: you learn to assess EU AI Act requirements on transparency and risk classes, design GDPR-compliant data flows with processing agreements and deletion policies, manage API keys securely and respect the licence terms of models and datasets.
  • 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

Building RAG pipelines

Implement ingestion, chunking, embeddings, vector search, hybrid retrieval and reranking, and generate answers with citations while honouring the access rights of source documents at retrieval time.

Automating evaluation

Create test sets with expected answers, define metrics for relevance, faithfulness and format, calibrate LLM-as-a-judge scoring and run evaluations in CI on every prompt or model change.

Developing agents and tools

Design tool definitions and MCP servers, implement agent loops with planning, tool calls and stop conditions, and build in permissions and human approval for consequential actions.

Securing structured output

Define JSON schemas for model responses, validate output, implement retry strategies on failure and make extraction tasks such as invoice or contract data robust. Faulty responses never reach downstream systems.

Observability and cost

Capture traces, token usage, latency and error rates, analyse problematic conversations and cut costs through caching, model routing and context reduction. Budgets and alerts per feature prevent surprises.

Defending against prompt injection

Test attack scenarios via manipulated documents, web pages or tool results, filter inputs and outputs, limit agent permissions and make red-teaming tests a fixed part of the pipeline.

Possible hands-on projects

Knowledge assistant with sources

A RAG chat over internal documents with citations, feedback buttons and an evaluation page that tracks answer quality across test questions.

Evaluation harness

A command-line tool that runs prompt variants against a test set, scores the results and clearly shows differences between versions.

Document extractor

A service that accepts PDFs, extracts structured data against a JSON schema, validates it and flags uncertain cases for manual review.

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 solid programming skills, ideally in Python or TypeScript, and experience with APIs; prior exposure to LLM APIs helps but is not required.

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

LLM applications often process personal and confidential data: you learn to assess EU AI Act requirements on transparency and risk classes, design GDPR-compliant data flows with processing agreements and deletion policies, manage API keys securely and respect the licence terms of models and datasets.

Suggested dates

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

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Mon, 7 December 2026 – Tue, 8 December 2026 Bremen Request
  • Thu, 7 January 2027 – Fri, 8 January 2027 Augsburg Request
  • Wed, 20 January 2027 – Thu, 21 January 2027 Online Request
  • Wed, 3 February 2027 – Thu, 4 February 2027 Salzburg Request
  • Thu, 18 February 2027 – Fri, 19 February 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.

How does AI engineering differ from classic machine learning?

In classic machine learning you train your own models on your own data. In AI engineering you usually work with pre-trained language models via APIs or self-hosted models and focus on context, retrieval, tools, evaluation and operations. Statistics and training know-how matter less, while software engineering skills matter all the more. That is precisely where this course comes in.

How do we know whether our LLM application is good enough?

With an evaluation set of realistic questions and expected results, a mix of rule-based checks, LLM-as-a-judge and human spot checks, plus monitoring in production. The key is running evaluations automatically on every change. In the course you build such a setup and learn to calibrate judge models.

How much does “Model Context Protocol (MCP) for AI Engineers” 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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