The Model Context Protocol (MCP): A Comprehensive Tutorial

The Model Context Protocol (MCP) is rapidly becoming the backbone for integrating AI applications with external data sources, tools, and systems. If you’re building AI-powered assistants, developer tools, or custom agents, understanding MCP will unlock new possibilities for context-aware, action-taking AI. This tutorial will guide you through the fundamentals, architecture, and practical usage of MCP.

What is the Model Context Protocol (MCP)?

MCP is an open standard introduced by Anthropic in late 2024 to standardize how AI models (like chatbots, IDE assistants, or agents) connect with external tools, databases, APIs, and other systems. Think of MCP as the USB-C port for AI: a universal connector that replaces a tangle of bespoke integrations with a single, standardized interface² ³ ⁵ ⁷.

Before MCP, integrating M different AI applications with N different tools required M×N custom integrations-an expensive and error-prone process. MCP transforms this into an M+N problem: each tool implements an MCP server, and each application implements an MCP client. This dramatically reduces development time and maintenance² ³.

Why MCP Matters

Standardization: One protocol to connect any AI app to any tool or data source³ ⁵.
Plug-and-play Ecosystem: Reusable integrations for popular services (GitHub, databases, etc.) are widely available³ ⁵.
Unlocks AI Potential: Enables AI to access real-time data and take meaningful actions, not just answer questions³ ⁶.
Vendor Flexibility: Easily switch between LLM providers or tools without rewriting integrations⁵.

MCP Architecture: The Core Components

MCP is built on a client-server architecture, inspired in part by the Language Server Protocol (LSP)⁴ ⁵ ⁶ ⁷:

Component	Role
Host	The AI application users interact with (e.g., Claude Desktop, IDEs)
MCP Client	Lives inside the Host, manages 1:1 connection to an MCP Server
MCP Server	Exposes tools, resources, and prompts to the AI via a standard API
Transport	Communication layer (STDIO for local, HTTP+SSE for remote)

All communication uses JSON-RPC 2.0 for standardized messaging4.

Key Concepts

Tools: Functions the AI can call (e.g., fetch weather, query GitHub issues)² ⁷.
Resources: Data sources the AI can access (e.g., a database, file contents)² ⁷.
Prompts: Predefined templates or workflows for common tasks² ⁷.

How MCP Works: Step-by-Step

Initialization:
The Host application creates MCP Clients, each connecting to a specific MCP Server. They perform a handshake to exchange capabilities and protocol versions² ⁷.
Discovery:
The Client requests a list of available Tools, Resources, and Prompts from the Server. The Server responds with descriptions and usage details² ⁷.
Context Provision:
The Host makes these capabilities available to the user or presents them in a format suitable for LLMs (e.g., JSON for function calling)².
Invocation:
When the AI determines it needs external data or action (e.g., “List open GitHub issues”), the Host instructs the Client to send a request to the appropriate Server² ⁷.
Execution:
The Server processes the request (e.g., calls the GitHub API) and returns the result to the Client² ⁷.
Response & Completion:
The Client relays the result to the Host, which incorporates it into the AI’s context or response to the user² ⁷.

Practical Example: Connecting Claude to GitHub

Suppose you want your AI assistant to fetch open issues from a GitHub repository:

MCP Server: Implements the logic to interact with the GitHub API.
MCP Client: Embedded in your AI app, connects to the GitHub MCP Server.
User Request: “What are the open issues in repo X?”
Flow: The AI recognizes it needs external data, instructs the Client to invoke the fetch_issues tool on the Server, receives the data, and responds to the user1 2 7.

Getting Started with MCP

Explore pre-built MCP servers for popular tools (GitHub, PostgreSQL, Notion)³ ⁵.
Implement an MCP client in your application to unlock access to all compatible servers² ⁴.
Follow best practices for security and data privacy, as MCP is designed to work within your infrastructure or across remote services⁵ ⁶.

Conclusion

MCP is revolutionizing how AI applications interact with the world-making them more dynamic, context-aware, and actionable. By adopting MCP, you join a growing ecosystem where integrations are standardized, reusable, and future-proof.

“MCP is like a USB-C port for AI applications. Just as USB-C provides a standard way to connect various devices, MCP provides a standard protocol that lets AI models connect to various data sources and tools.”
DiamantAI3

For more in-depth guides and demos, check out the official MCP documentation and community tutorials1 5.