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DevelopmentIntermediate33 lessons14–18 hours
MCP & AI Tool Integration
Master the Model Context Protocol (MCP) to connect AI models to any tool, API, or data source. Build custom MCP servers and integrate them into production workflows.
What You'll Learn
Understand the MCP protocol: transports, messages, and lifecycle
Build MCP servers in TypeScript and Python from scratch
Define tools with precise schemas and rich descriptions
Implement resource management for files, databases, and APIs
Connect MCP servers to databases like PostgreSQL and MongoDB
Integrate third-party APIs as MCP tools with proper error handling
Secure MCP servers with authentication, authorization, and rate limiting
Deploy MCP servers to production with Docker and cloud platforms
Outcomes
- Build custom MCP servers that connect AI to any API or database
- Implement authentication, authorization, and security for tool access
- Deploy production MCP servers with logging, monitoring, and error handling
- Design tool schemas that give AI models the right capabilities
Prerequisites
- -TypeScript/Python fundamentals
- -Understanding of REST APIs and JSON
Projects You'll Build
- Build a database-connected MCP server
- Create an API integration MCP server with OAuth
- Deploy a multi-tool MCP server to production
Course Curriculum
Module 1: MCP Fundamentals
- 1.1What is MCP and why it matters for AI integration
- 1.2The three primitives: tools, resources, and prompts
- 1.3MCP architecture: clients, servers, and transports
- 1.4How MCP compares to REST APIs and GraphQL
- 1.5Setting up your MCP development environment
- 1.6Your first MCP server: a simple calculator tool
- 1.7Connecting your server to Claude Desktop and Claude Code
Module 2: Building MCP Servers
- 2.1Project structure and TypeScript SDK deep dive
- 2.2Defining tools: names, descriptions, input schemas
- 2.3Implementing tool handlers with validation and error handling
- 2.4Resources: exposing files, data, and dynamic content
- 2.5Prompts: packaging reusable prompt templates
- 2.6Stdio vs SSE transports: when to use which
- 2.7Testing MCP servers with the MCP Inspector
Module 3: Advanced Tool Patterns
- 3.1Database tools: query, insert, update with schema introspection
- 3.2File system tools: read, write, search with glob patterns
- 3.3API wrapper tools: turning any REST API into an MCP tool
- 3.4Multi-step tools: workflows that require sequential operations
- 3.5Streaming results for long-running operations
- 3.6Tool composition: building complex tools from simple ones
Module 4: Security & Authentication
- 4.1Authentication patterns: API keys, OAuth, and JWT
- 4.2Authorization: role-based access to tools and resources
- 4.3Input sanitization and injection prevention
- 4.4Rate limiting and abuse prevention
- 4.5Audit logging: tracking every tool invocation
- 4.6Secrets management: handling API keys and credentials safely
Module 5: Production Deployment
- 5.1Dockerizing your MCP server for deployment
- 5.2Deploying to cloud platforms (AWS, GCP, Railway)
- 5.3Health checks, monitoring, and alerting
- 5.4Versioning your MCP server API
- 5.5Publishing to MCP registries for discovery
- 5.6Performance optimization: caching, connection pooling, batching
- 5.7Maintaining MCP servers: updates, deprecation, and migration
AI isn't slowing down.
Neither should you.
Every week you wait, the gap widens. The people who invest in learning AI now will be the ones leading teams, building companies, and staying ahead of the curve. This is your moment — don't let it pass.