Training course · Model Context Protocol (MCP) · Go

Model Context Protocol (MCP) for Go 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

This course connects Model Context Protocol (MCP) with the everyday processes of Go. 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.

Go is the language behind many cloud-native tools, microservices, CLIs and infrastructure components. Its strengths are simplicity, fast compilation and built-in concurrency – yet goroutines, channels, context handling and error handling all demand discipline. AI coding assistants and agents suit Go particularly well: the compiler, go vet and the race detector provide fast, unambiguous feedback that agents can iterate on. Typical tasks include table-driven tests, gRPC and HTTP handlers generated from specifications, tracking down race conditions or goroutine leaks and refactoring towards clean interfaces. This course shows you how to get idiomatic Go from AI and how to verify the results with Go’s own tooling.

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?

  • Go developers in back-end and platform teams
  • Developers of cloud-native tools, operators and CLIs
  • Site reliability engineers maintaining their own Go code
  • Software architects designing high-performance microservices

Typical challenges we address

  • Subtle concurrency bugs such as race conditions, deadlocks and goroutine leaks
  • Repetitive boilerplate in error handling, handlers and tests
  • Inconsistent project layouts and interface boundaries in growing codebases
  • Difficult performance analysis of memory allocations and latency

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.
  • Have AI agents produce idiomatic Go code, tests and services that follow Go conventions.
  • Use AI deliberately to analyse concurrency and performance problems.
  • Verify generated code with the compiler, go vet, the race detector, linting and benchmarks.

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: Go
  • Writing table-driven tests
  • Implementing gRPC and HTTP services
  • Finding concurrency bugs
  • Idiomatic refactoring
  • Profiling performance
  • Building CLIs and tools
Module 7 Data protection, quality & transfer to daily work
  • Go tooling often runs deep in the infrastructure, so keep kubeconfigs, tokens and certificates strictly out of an AI assistant’s context. Check new modules for provenance, licence and known vulnerabilities, for instance with govulncheck, and record dependencies in an SBOM as required under the Cyber Resilience Act for products with digital elements.
  • 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

Writing table-driven tests

The agent generates idiomatic table-driven tests with subtests, test helpers and fakes for interfaces, runs go test with the race detector and adds missing edge cases until coverage is where you want it.

Implementing gRPC and HTTP services

Protobuf definitions or OpenAPI specs become handlers, middleware for logging and authentication, context propagation with timeouts and clean error returns. Integration tests exercise the endpoints against realistic requests and failure cases.

Finding concurrency bugs

The AI analyses race detector output, goroutine dumps and code using channels or mutexes, explains the cause of deadlocks or leaks and proposes fixes using errgroup or context cancellation.

Idiomatic refactoring

Large packages are untangled, small consumer-side interfaces introduced, errors wrapped and checked with errors.Is, and code aligned with golangci-lint rules. Existing tests keep behaviour safe throughout.

Profiling performance

pprof profiles and benchmark results are interpreted, hotspots and unnecessary allocations identified, and optimisations backed by before-and-after benchmarks. Improvements become measurable rather than merely assumed.

Building CLIs and tools

The assistant creates command-line tools with Cobra or the standard library, including configuration, structured logging, cross-compilation and release automation. Tests, help texts and clear command documentation are included.

Possible hands-on projects

Health check aggregator

A Go service that polls the health endpoints of many services concurrently, merges the results and exposes them as JSON and Prometheus metrics.

Log shipper CLI

A lean command-line tool that tails log files, filters entries and ships them in batches to an HTTP endpoint – with backoff and graceful shutdown.

Kubernetes resource report

A CLI built on client-go that reads resource requests and limits across all deployments and presents anomalies in a clear table.

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. Hands-on Go experience including goroutines, interfaces and modules, plus a working knowledge of Git and containers.

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

Go tooling often runs deep in the infrastructure, so keep kubeconfigs, tokens and certificates strictly out of an AI assistant’s context. Check new modules for provenance, licence and known vulnerabilities, for instance with govulncheck, and record dependencies in an SBOM as required under the Cyber Resilience Act for products with digital elements.

Suggested dates

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

  • Mon, 23 November 2026 – Tue, 24 November 2026 Online Request
  • Wed, 2 December 2026 – Thu, 3 December 2026 Augsburg Request
  • Mon, 14 December 2026 – Tue, 15 December 2026 Online Request
  • Wed, 20 January 2027 – Thu, 21 January 2027 Kaiserslautern Request
  • Tue, 26 January 2027 – Wed, 27 January 2027 Online Request
  • Thu, 11 February 2027 – Fri, 12 February 2027 Bremen 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.

Does AI really write idiomatic Go?

With good context, yes. Without guidance, models sometimes drift towards patterns from other languages, such as large interfaces or excessive abstraction. On the course you capture your Go conventions, linter configuration and example code so the assistant favours simple, idiomatic solutions, and you verify the output with golangci-lint.

Can AI reliably detect race conditions?

AI is a powerful analysis aid but no substitute for the race detector. The combination works best: the agent runs tests with the race detector, interprets the reports and proposes fixes. You learn to set up that loop and to critically assess any suggested synchronisation changes before accepting them.

How much does “Model Context Protocol (MCP) for Go 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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