Architecture Guide
Model Context Protocol (MCP)
MCP is an open standard for connecting AI models to external tools and data sources — databases, file systems, APIs, SaaS products. Instead of building a custom integration for every tool an AI assistant needs, you build (or use) one MCP server, and any MCP-compatible client can talk to it.
What is MCP?
MCP follows a client-server model. An MCP server wraps a specific capability — your company's database, a set of internal APIs, a file system, a third-party SaaS product — and exposes it in a standardized way. An MCP client (Claude Desktop, Claude Code, or any other MCP-compatible AI application) connects to one or more servers and gives the model access to what they expose.
What a server exposes falls into a few primitives: tools (functions the model can call, like a function-calling definition), resources (data the model can read, like files or database records), and prompts (reusable prompt templates the server can supply to the client). The model decides when to call a tool or read a resource based on the user's request, the same way it would with regular function calling — MCP just standardizes how that connection is made and discovered.
Without MCP vs with MCP
Without: N apps × M tools = N×M custom integrations
With MCP: N apps + M servers = N+M connections
Why it matters
One standard connector, not one per tool
Build an MCP server once for your database or internal API, and every MCP-compatible client — Claude Desktop, Claude Code, other AI apps — can use it without custom glue code.
Tools and resources are discoverable
A client connecting to a server can list what it offers at runtime. The model doesn't need every capability hardcoded into its system prompt ahead of time.
It's not a replacement for function calling
MCP sits on top of the same tool-calling mechanism models already use. It standardizes discovery and connection, not the underlying call-a-function-get-a-result loop.
Most concrete in agentic coding tools
Claude Code's MCP servers are where the pattern shows up most in daily use — connecting a coding agent to your ticketing system, database, or internal docs without writing a custom integration.
Articles
Core Concept
What is the Model Context Protocol (MCP)? A Plain-English Guide
MCP is Anthropic's open standard for connecting AI assistants to external tools and data sources — what it is, how it works, and why it matters.
Deep Dive
MCP — A Deep Dive Beyond the Basics
The 3 primitives, setting up real MCP servers, building your own in Python, and which servers are worth using in 2026.
Tutorial
MCP Protocol Tutorial: Build Your First MCP Server
Build a working MCP server from scratch and connect it to Claude — no prior MCP experience needed.
Tutorial
Build Your First MCP Server in Python
A real MCP server that exposes live API data to Claude, under 100 lines of Python. Covers tools, resources, and connecting to Claude Code.
Comparison
MCP vs Function Calling vs Tool Use — What's the Difference?
A clear breakdown of how MCP, function calling, and tool use relate, and which to reach for in your AI project.
Comparison
Structured Outputs and Tool Calling Across OpenAI, Claude and Gemini
The concepts transfer everywhere; the parameter names and response shapes don't. A working side-by-side across all three.
Reference
The 25 Best MCP Servers in 2026
A practical, category-by-category guide to the MCP servers that are actually reliable in production.
Related Lessons
Structured lessons on MCP and the techniques that work alongside it.
Related Guides
See MCP in Action
The Claude Code track covers MCP servers in the context where they're most concretely useful — connecting a coding agent to your own tools and data.
Go to Claude Code Track