MCP Development in .NET: Building Tool-Integrated AI Systems
Certificate of Completion by Code.Hub
This training introduces developers to building AI-powered applications in .NET using the Model Context Protocol (MCP), enabling seamless integration between Large Language Models and external tools, APIs, and data sources. Participants will learn how to design and implement MCP servers and clients in .NET, exposing structured capabilities such as functions, data access, and workflows to LLMs. The course covers tool definition, schema design, context management, and secure execution of AI-triggered operations. Through hands-on labs, learners will build intelligent systems where LLMs orchestrate actions such as querying databases, invoking services, and automating workflows. The training also explores integration with platforms like Azure OpenAI and local LLM runtimes, emphasizing interoperability and extensibility. Special focus is given to reliability, validation, observability, and governance in production-grade MCP systems. By the end of the course, participants will be able to design robust, tool-driven AI applications using .NET as the orchestration backbone.
By the end of this module, participants will be able to:
- Design and implement MCP servers in .NET exposing tools, resources, and structured capabilities
- Build MCP clients that enable LLMs to interact with APIs, databases, and external systems
- Integrate MCP-based systems with Azure OpenAI or local LLMs for tool-augmented reasoning
- Apply validation, security, and governance controls for safe execution of AI-driven actions
- Architect scalable, production-ready AI systems using MCP and modern .NET patterns
Foundations of MCP & AI Tooling LLM Tool Integration Concepts
Introduction to MCP
- What is MCP and why it matters
- MCP vs function calling vs plugins
- Use cases and architecture overview
AI Practice: Use AI to design a tool-integrated workflow for a business scenario
MCP Core Concepts
- Tools, resources, and prompts
- JSON schema definitions
- Capability exposure
AI Practice: Generate tool schemas using AI for a sample API
MCP Server Development .NET Implementation
Building MCP Servers in .NET
- Creating MCP-compatible endpoints
- Tool registration and execution
- Dependency injection patterns
AI Practice: Scaffold an MCP server using AI-assisted code generation
Tool Integration & Execution
- Connecting APIs and databases
- Handling tool requests and responses
- Mapping outputs to LLM context
AI Practice: Implement a tool that queries a database using AI-generated logic
MCP Clients & Orchestration LLM Integration
MCP Client & LLM Integration
- Connecting MCP with LLMs
- Tool selection and invocation
- Prompt + schema orchestration
AI Practice: Build a client that invokes MCP tools via LLM decisions
Reliability & Production Readiness Security & Observability
Validation, Security & Capstone
- Input/output validation
- Guardrails and safe execution
- Logging and monitoring
- Final project implementation
AI Practice: Build a secure, production-ready MCP-based system
Roles:
- .NET Developer
- AI/ML Engineer
- Solutions Architect
Seniority:
- Mid to Senior Level
- Strong experience in C# and .NET, including Web APIs, dependency injection, and asynchronous programming
- Familiarity with REST APIs, JSON schemas, and basic concepts of LLMs and prompt engineering
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
Interested for
