Welcome to Model Context Protocol! This course will teach you about MCP - an open standard that enables seamless integration between AI applications and data sources. MCP is revolutionizing how AI systems access and interact with external context.
What is Model Context Protocol (MCP)?
Model Context Protocol (MCP) is an open protocol developed by Anthropic that standardizes how AI applications connect to data sources and tools. It provides a universal way for LLMs to access context from various systems - databases, APIs, file systems, and more - through a unified interface.
Why MCP Matters
Before MCP, every integration required custom code:
- Fragmentation - Each AI app used different methods to connect to data
- Duplication - Same integrations built multiple times
- Maintenance - Breaking changes required updates everywhere
- Limited Adoption - High barrier to adding new data sources
MCP solves these problems with a standardized protocol:
- Universal Standard - One protocol for all integrations
- Reusability - Build once, use everywhere
- Ecosystem - Growing library of pre-built servers
- Flexibility - Works with any LLM or AI application
Core MCP Concepts
MCP is built around several key concepts:
- MCP Servers - Expose data sources and capabilities
- MCP Clients - AI applications that consume MCP servers
- Resources - Data that can be read (files, database records, API responses)
- Tools - Actions that can be executed (function calls, API requests)
- Prompts - Pre-defined prompt templates
- Sampling - LLM completion requests from servers
MCP Architecture
The MCP architecture consists of:
- Host Application - AI app (Claude Desktop, IDEs, custom apps)
- MCP Client - Built into the host, communicates via MCP protocol
- MCP Server - Exposes resources and tools from a data source
- Data Source - The underlying system (database, API, file system)
Key Features
- Bidirectional Communication - Servers can request completions from the LLM
- Secure by Default - Built-in authentication and authorization
- Transport Agnostic - Works over stdio, HTTP/SSE, or WebSockets
- Language Independent - SDKs available for multiple languages
- Extensible - Add custom capabilities as needed
MCP vs. Other Approaches
Understanding where MCP fits:
- Function Calling - MCP standardizes how functions are exposed and called
- RAG - MCP can be used to implement RAG by exposing vector databases
- APIs - MCP wraps APIs in a standard interface for AI consumption
- Plugins - MCP provides a more structured alternative to custom plugins
Popular MCP Servers
The MCP ecosystem is growing rapidly:
- File System - Access local files and directories
- PostgreSQL - Query and manipulate databases
- GitHub - Interact with repositories and issues
- Google Drive - Access documents and files
- Slack - Read and send messages
- Brave Search - Web search capabilities
- Memory - Persistent knowledge graphs
Real-World Use Cases
MCP enables powerful integrations:
- Development Tools - AI assistants with codebase access
- Data Analysis - LLMs querying business databases
- Automation - AI agents controlling enterprise systems
- Research - Accessing academic papers and datasets
- Customer Support - AI with access to customer data and CRM
- Content Management - AI working with CMS and knowledge bases
Building with MCP
The development workflow involves:
- Choose a Data Source - Identify what you want to expose
- Create MCP Server - Implement using MCP SDK
- Define Resources/Tools - Expose capabilities through MCP
- Test - Verify with MCP Inspector
- Deploy - Make available to MCP clients
- Integrate - Connect from AI applications
MCP SDKs and Tools
- TypeScript SDK - Official SDK for Node.js
- Python SDK - Official Python implementation
- MCP Inspector - Debug and test MCP servers
- Claude Desktop - Built-in MCP client support
- Cursor IDE - MCP integration for coding
What You'll Learn
This comprehensive course covers:
- MCP protocol fundamentals and architecture
- Building MCP servers from scratch
- Implementing resources, tools, and prompts
- Connecting to various data sources (databases, APIs, files)
- Security and authentication best practices
- Integrating MCP into AI applications
- Testing and debugging MCP servers
- Deploying production MCP servers
- Advanced patterns and use cases
Prerequisites
- Strong programming skills (TypeScript or Python)
- Understanding of APIs and web protocols
- Familiarity with LLMs and AI agents
- Knowledge of databases and data sources
- Experience with async programming
By the end of this course, you'll be able to build production-grade MCP servers that seamlessly connect AI applications to any data source or tool.
Let's master Model Context Protocol!