Model Context Protocol (MCP) for AI Integration Training Course
The Model Context Protocol (MCP) is an open standard designed to link AI applications with external tools, data sources, and business systems.
This instructor-led live training, available either online or onsite, targets beginner to intermediate-level AI professionals looking to utilise MCP to build practical integrations between AI assistants and enterprise systems.
Upon completion of this training, participants will be able to:
- Articulate the purpose, value, and core concepts of MCP.
- Understand how MCP clients, servers, tools, resources, and prompts operate together.
- Establish and test a fundamental MCP-enabled workflow.
- Apply good practices regarding security, governance, and implementation.
Course Format
- Interactive lecture and discussion.
- Hands-on exercises and guided practice.
- Live lab sessions focused on realistic integration scenarios.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
MCP Fundamentals and Business Value
- What MCP is and why organisations are adopting it.
- Problems MCP helps solve in AI integration.
- MCP compared with direct API integration and other tool connection approaches.
- Common enterprise use cases and expected benefits.
Core Architecture and Components
- Roles of hosts, clients, and servers.
- How tools, resources, and prompts are used.
- Request and response flow in a typical MCP interaction.
- Local and remote deployment patterns.
Setting Up a Basic MCP Workflow
- Preparing the working environment.
- Reviewing a simple MCP server configuration.
- Connecting a client to an MCP server.
- Running and validating a basic workflow.
Designing Useful MCP Integrations
- Selecting the right capability for a business scenario.
- Structuring tools for safe and useful actions.
- Using resources to provide relevant context.
- Using prompts to improve consistency and usability.
Security, Governance, and Operations
- Access control, permissions, and authentication considerations.
- Handling sensitive business data safely.
- Trust, approval, and oversight practices.
- Monitoring, maintenance, and operational good practices.
Implementation Planning and Next Steps
- Identifying realistic use cases for an initial rollout.
- Key design decisions and practical trade-offs.
- Planning adoption in enterprise environments.
- Course review, summary, and next steps.
Requirements
- Fundamental understanding of AI assistants, APIs, and business application workflows.
- Experience using web applications, developer tools, or enterprise software platforms.
- Basic technical or programming experience.
Audience
- AI engineers and application developers.
- Solution architects and technical leads.
- Product teams and IT professionals evaluating AI integration options.
Open Training Courses require 5+ participants.
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Provisional Upcoming Courses (Require 5+ participants)
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