Training course · Cursor · API Development

Cursor for API Developers

Work productively with the AI code editor Cursor: agent mode, rules, codebase context, Tab completion, background agents and review.

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

“Cursor for API Developers” is a hands-on course 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. Rather than learning Cursor in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from API Development.

Cursor is an AI code editor built on VS Code that weaves language models deeply into everyday development. Alongside context-aware Tab completion, it offers an agent mode that searches files on its own, plans changes across the whole project, runs terminal commands and fixes errors iteratively. In this two-day course you will learn to use Cursor with purpose: choosing the right interaction mode, steering context deliberately and combining AI assistance with your existing workflow of version control, automated testing and peer code review.

A key focus is making Cursor work for teams. Project rules let you capture architectural decisions, naming conventions and testing strategies so that the agent produces consistent results. You will learn to make good use of codebase indexing, targeted references to files, documentation and symbols, and MCP integrations. You will also delegate tasks to background agents, review their output as pull requests and set up a review process that takes quality, security and data protection seriously across the organisation.

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 choose the right Cursor mode for each task and use it efficiently.
  • You steer codebase context precisely and get more accurate suggestions as a result.
  • You write project rules that reliably enforce your team’s conventions.
  • You use agent mode and background agents for features and refactorings with verifiable results.
  • 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 Setting up Cursor and understanding its modes
  • Installation and migration of VS Code settings and extensions
  • Differences between Tab completion, inline edits, Ask mode and Agent mode
  • Choosing a model per task: balancing speed, depth and cost
  • Privacy mode and organisation-level settings
Module 2 Steering codebase context
  • How codebase indexing works and where its limits lie
  • Referencing files, folders, symbols, documentation and web sources precisely
  • Using ignore files to exclude sensitive or irrelevant content
  • Avoiding overlong chats and restarting tasks with fresh context
Module 3 Rules for consistent team results
  • Creating and versioning project rules and scoping them by file pattern
  • Expressing architecture, style and testing conventions as effective rules
  • Separating user rules from team rules
  • Refining rules iteratively based on the agent’s typical mistakes
Module 4 Agent mode in practice
  • Building features step by step with plan, implementation and tests
  • Running multi-file refactorings safely with checkpoints
  • Approving terminal commands deliberately and feeding error output back
  • Connecting MCP servers for issue trackers, databases or browser testing

Day 2

Module 5 Background agents, review and governance
  • Delegating tasks to background agents and running them in parallel
  • Reviewing results as branches or pull requests and steering follow-ups
  • Using AI-assisted code review alongside human review
  • Addressing security risks, secrets and licensing in enterprise use
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

You program regularly in at least one language and are familiar with Git and an IDE such as VS Code. 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

Your own laptop with Cursor, Git and your usual development environment installed; a Cursor account with access to agent features is recommended.

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
  • Mon, 30 November 2026 – Tue, 1 December 2026 Munich Request
  • Wed, 9 December 2026 – Thu, 10 December 2026 Online Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Hanover Request
  • Tue, 19 January 2027 – Wed, 20 January 2027 Online Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Berlin Request

Frequently asked questions

Do I need prior VS Code experience to use Cursor?

It helps, but it is not essential. Cursor is built on VS Code, so navigation, shortcuts and extensions feel familiar and existing settings can be imported. If you currently work with JetBrains IDEs or other editors, a short introduction during the course gets you up to speed quickly so you can concentrate on the AI features.

How do I prevent confidential code from reaching third parties?

Cursor offers a privacy mode and organisation settings that restrict how code is stored by the provider. You can also use ignore files to exclude sensitive areas from indexing and context. In the course we discuss how to align these options with internal policies, data protection requirements and sound handling of secrets.

What do rules add compared with well-written prompts?

A prompt applies to one request only, whereas rules persist and apply to the whole team. They capture architectural decisions, preferred libraries, testing strategies and style guidelines, so nobody has to repeat that information and the agent produces code that fits your codebase. Rules are versioned with the project and evolve together with it.

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 “Cursor 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.

More courses for API Developers

More Cursor courses

All 32 Cursor courses

Cursor · MunichCursor · SalzburgCursor · BerlinCursor · AugsburgCursor · HanoverCursor · BremenCursor · StuttgartCursor · KaiserslauternCursor · Online