MCP Tools Mastery
Master the Model Context Protocol (MCP) to connect AI assistants to databases, APIs, and tools. Build custom MCP servers in Python and TypeScript with 8 hands-on lessons.
What You'll Learn
- Explain the MCP architecture: hosts, clients, servers, and transport layers
- Build a functional MCP server that exposes tools, resources, and prompts
- Implement the three MCP primitives — tools, resources, and prompts — for different use cases
- Design MCP servers that connect AI assistants to databases, APIs, and file systems
- Apply security best practices including OAuth 2.1, input validation, and least-privilege access
- Create a multi-tool MCP server and deploy it for production use
Course Syllabus
Prerequisites
- Basic programming experience (Python or TypeScript)
- Familiarity with command-line tools and JSON
- An AI assistant that supports MCP (Claude Desktop, Claude Code, or ChatGPT)
What You’ll Learn
Every AI assistant hits the same wall: it can’t access your data, call your APIs, or interact with your tools. The Model Context Protocol (MCP) breaks that wall down.
MCP is the open standard — created by Anthropic and now governed by the Linux Foundation — that lets AI assistants connect to any external system: databases, file systems, APIs, SaaS tools, and custom services. Instead of building one-off integrations for each AI platform, you build one MCP server and it works everywhere — Claude, ChatGPT, Gemini, and any future MCP-compatible client.
This course teaches you to build MCP servers from scratch. You’ll understand the architecture, implement all three primitives (tools, resources, and prompts), connect to real systems like databases and APIs, secure your servers for production, and deploy them.
Who This Course Is For
- Developers who want to give AI assistants access to their company’s data and tools
- AI engineers building agentic workflows that need real-world tool access
- DevOps/platform engineers standardizing how AI connects to infrastructure
- Technical product managers evaluating MCP for their team’s AI strategy
Course Structure
8 lessons, each 12-15 minutes, with working code you can run locally. By the end, you’ll have a production-ready MCP server deployed and connected to your AI assistant.
Frequently Asked Questions
Do I need to be an advanced programmer?
No. Basic Python or TypeScript experience is enough. MCP SDKs handle the protocol details — you focus on what your tools do, not how the protocol works. We build servers step by step with complete code examples.
Which AI assistants support MCP?
Claude Desktop, Claude Code, ChatGPT, and Gemini all support MCP. The protocol is an open standard maintained by the Linux Foundation's Agentic AI Foundation, so adoption is growing across all major platforms.
Can I use MCP at work for enterprise projects?
Yes. MCP is production-ready with OAuth 2.1 authentication, fine-grained authorization, and OWASP-reviewed security practices. Many enterprises already use MCP servers for database access, CRM integration, and internal tooling.
What's the difference between MCP and regular API integrations?
Regular APIs require custom code for every AI-tool connection. MCP standardizes the interface — build one MCP server and any MCP-compatible AI client can use it. Think USB-C: one connector, many devices.