Content:

This training is aimed at software engineers, platform engineers, tech leads, and architects who want to expose internal systems to AI agents in a controlled way. The course is framework- and programming-language-independent; for in-house courses, practical examples can be adapted to the existing tool and API landscape.

– MCP Fundamentals and Architecture:
... - Understand Model Context Protocol (MCP), hosts, clients, and servers
... - Distinguish tools, resources, and prompts clearly
... - Classify request/response flows, capabilities, and transport options
... - Hands-On: Inspect existing MCP servers and use them with an agent

– Enterprise Use Cases and Integration Boundaries:
... - Suitable candidates: internal APIs, operational tools, data sources, and workflows
... - Distinguish chatbot, agent, API gateway, and MCP server responsibilities
... - Choose integration depth consciously: read, suggest, execute, escalate
... - Hands-On: Select a use case and define integration boundaries

– Tool Design for AI Agents:
... - Design tool names, descriptions, and input schemas for agent use
... - Develop small, safe actions instead of overly broad universal tools
... - Consider idempotency, validation, error cases, and response structures
... - Hands-On: Design tool contracts for an enterprise service

– Resources and Context Access:
... - Provide read-only context sources through MCP
... - Control data scope, freshness, and visibility
... - Shape structured responses for better agent decisions
... - Hands-On: Implement resource access for project-relevant data

– Security, Permissions, and Governance:
... - Plan least privilege, secrets, environment boundaries, and audit logs
... - Classify authentication and authorization in MCP scenarios
... - Recognize prompt injection, tool misuse, and data leakage risks
... - Design approval and human-in-the-loop patterns for sensitive actions

– MCP Server Implementation:
... - Build server structure, SDK usage, tool handlers, and resource handlers
... - Connect external APIs and internal services
... - Integrate logging, error handling, and configuration
... - Hands-On: Build an MCP server for a realistic business or engineering process

– Testing and Quality Assurance:
... - Test tool contracts automatically
... - Write positive and negative test cases for parameters, errors, and permissions
... - Validate agent behavior with realistic prompts
... - Hands-On: Create a test and review checklist for MCP integrations

– Operations and Maintainability:
... - Plan versioning, rollout, monitoring, and ownership
... - Discuss integration into existing development and platform processes
... - Create documentation for users, agent configurations, and operations teams
... - Assess operational risks and next steps for production introduction

The course combines short conceptual inputs with live coding and many practical exercises. The goal is not only to build an MCP server, but to develop solid engineering judgment for secure enterprise integrations with AI agents.

Disclaimer: The actual course content may vary from the above, depending on the trainer, implementation, duration and constellation of participants.

Whether we call it training, course, workshop or seminar, we want to pick up participants at their point and equip them with the necessary practical knowledge so that they can apply the technology directly after the training and deepen it independently.