Training course · Model Context Protocol (MCP) · API Development

Model Context Protocol (MCP) for API Developers

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 API Developers” is designed for roles such as Backend developers building REST, GraphQL or gRPC interfaces, API architects and owners of API governance and Integration developers connecting partners and platforms and shows step by step how to use Model Context Protocol (MCP) productively, safely and transparently in API Development – 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.

APIs form the backbone of modern software landscapes and are increasingly the interface through which AI agents interact with systems. AI coding assistants speed up the entire API lifecycle, from drafting an OpenAPI or AsyncAPI specification to generating server stubs, clients and contract tests, through to documentation and versioning. At the same time, demands for consistency, security and backward compatibility keep rising, because APIs are consumed by many teams, partners and automated agents. In this course you learn to practise API-first development with AI support, enforce design guidelines automatically, catch breaking changes early and expose existing interfaces to AI agents through the Model Context Protocol or tool definitions.

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?

  • Backend developers building REST, GraphQL or gRPC interfaces
  • API architects and owners of API governance
  • Integration developers connecting partners and platforms
  • Platform engineering teams running API gateways and developer portals
  • Developers making company data available to AI agents

Typical challenges we address

  • Inconsistent API design across teams with differing conventions for errors, pagination and versioning
  • Specification, implementation and documentation drift apart
  • Breaking changes are only discovered by consumers
  • Securing authentication, authorisation and rate limiting as users and agent traffic grow

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.
  • Practise API-first development with AI support, from specification to clients, tests and documentation.
  • Automate design governance, contract testing and breaking-change detection in your pipeline.
  • Make existing APIs available to AI agents securely and in a controlled way, for example via the Model Context Protocol.

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: API Development
  • Generating OpenAPI specs
  • Generating servers and clients
  • Building contract tests
  • Detecting breaking changes
  • Reviewing API security
  • Opening APIs to AI agents
Module 7 Data protection, quality & transfer to daily work
  • APIs frequently carry personal and confidential data: you learn to create specifications and sample data without real customer records, keep API keys and tokens out of the AI context and apply data protection by design under the GDPR as well as NIS2 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

Generating OpenAPI specs

Draft complete OpenAPI specifications with schemas, examples, error objects and security schemes from requirements, domain models or existing code, and check them against your style guide rules.

Generating servers and clients

Derive controller scaffolding, validation logic and type-safe client SDKs for several languages from a specification, keeping generated parts cleanly separated from hand-written business logic. Regenerating after spec changes is simple.

Building contract tests

Define consumer-driven contracts, generate test cases for edge values, error paths and authentication, and run them automatically in CI against both the specification and the live implementation.

Detecting breaking changes

Compare two versions of a specification, identify incompatible changes, write migration notes for consumers and plan deprecation strategies using versioning and sunset headers. The check runs on every pull request.

Reviewing API security

Analyse endpoints for typical weaknesses from the OWASP API Security Top 10, such as broken object-level authorisation, mass assignment or missing rate limits, and propose fixes.

Opening APIs to AI agents

Expose existing interfaces as MCP servers or tool definitions, phrase descriptions and parameters so agents use them correctly and scope permissions so agents can only perform approved actions.

Possible hands-on projects

API linter with house rules

A command-line tool that checks OpenAPI files against your design guidelines and posts clear improvement suggestions directly on the pull request.

Mock server from spec

A small service that generates realistic sample responses from a specification so frontend teams can work in parallel with backend development.

MCP server for an internal API

An MCP server that exposes selected endpoints of an internal REST API as tools for AI assistants, with authentication and logging.

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 build backend applications in a mainstream language such as Java, C#, TypeScript, Python or Go and understand HTTP, JSON and REST fundamentals.

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

APIs frequently carry personal and confidential data: you learn to create specifications and sample data without real customer records, keep API keys and tokens out of the AI context and apply data protection by design under the GDPR as well as NIS2 requirements.

Suggested dates

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

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Tue, 1 December 2026 – Wed, 2 December 2026 Munich Request
  • Mon, 7 December 2026 – Tue, 8 December 2026 Online Request
  • Mon, 14 December 2026 – Tue, 15 December 2026 Stuttgart Request
  • Tue, 12 January 2027 – Wed, 13 January 2027 Online Request
  • Thu, 28 January 2027 – Fri, 29 January 2027 Berlin 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.

Is spec-first still worth it when AI writes code so quickly?

More than ever. A precise specification is the best context you can give an AI assistant: it pins down data structures, error cases and security requirements, serves as a contract for consumers and underpins your tests. Without one, AI quickly produces inconsistent endpoints. In the course you use the spec as the single source for generation and validation.

What is the difference between a REST API and an MCP server?

A REST API is aimed at developers and applications, whereas an MCP server exposes capabilities in a standardised format for AI assistants and agents, including descriptions the model can interpret. An MCP server is often a thin layer on top of existing APIs. You learn when that step pays off and how to implement it securely.

How much does “Model Context Protocol (MCP) for API Developers” 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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