Course Objective & Scope
Writing custom glue code for every LLM application and internal system creates an unsustainable maintenance burden at enterprise scale. The Model Context Protocol (MCP) exposes tools, data sources and prompt templates to agents through a single open standard.
In this hands-on course participants explore the MCP architecture (hosts, clients, servers; tools, resources, prompts), build a custom MCP server in Python or TypeScript wrapping an internal API or database, implement OAuth and role-based access control, and leave with a production-ready MCP server template.
Who Should Attend?
- Backend and platform engineers exposing enterprise systems to AI applications
- AI engineers and solution architects building agent infrastructure
- API management and integration teams
- Prerequisite: API development experience with Python or TypeScript
Key Takeaways
- Explain the host, client and server roles and message flow in MCP
- Match tools, resources and prompts to the right integration scenarios
- Build a custom MCP server wrapping enterprise APIs and data sources
- Configure Streamable HTTP transport, authentication and permission boundaries
- Create a reusable internal MCP server template and security checklist
Curriculum
MCP Architecture and Core Primitives
- Host, Client and Server concepts; JSON-RPC messaging
- Tools, Resources, Prompts and Sampling capabilities
Building a Custom MCP Server
- Server setup with the Python (FastMCP) and TypeScript SDKs
- Wrapping internal REST APIs and SQL sources as MCP tools
Transport Layers and Deployment
- Local stdio versus Streamable HTTP for remote servers
- Container packaging, scaling and central MCP gateway patterns
Authentication, Authorisation and Security
- OAuth 2.1, user-scoped token forwarding and least privilege
- Defences against tool poisoning, rug-pulls and indirect prompt injection
Agent and Client Integration
- Connecting from Claude, OpenAI Agents SDK, LangGraph and IDE clients
- Logging, error handling and enterprise MCP checklist
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