Model Context Protocol (MCP) is an open standard Anthropic released in November 2024 that lets AI applications and tools communicate through one shared language instead of custom, one-off connectors. It solves the M times N integration problem: with 5 AI apps and 10 tools, 50 custom integrations drop to 15 shared connections.
What you will be able to do
- Explain the M times N integration problem and why it made connecting AI apps to tools expensive before MCP existed.
- Calculate how many custom integrations a given number of AI apps and tools requires, with and without MCP.
- Identify who built MCP, when Anthropic released it, and which existing protocol (LSP) it was modeled on.
- List the four specific ways integrations broke before MCP: too many signals to learn, breakage on updates, vendor lock-in, and missing safety checks.
- Describe how MCP's client and server model turns adding a new AI app or tool into a single new connection instead of a full new set of connections.
Before you start
- Have read or skimmed Lesson 1, "What Is MCP? Model Context Protocol Explained Simply," since this lesson builds on that definition.
- Understand at a basic level that AI applications connect to external tools or data sources (files, APIs, calculators) to do useful work.
- No coding or setup required for this lesson; it is conceptual and prepares you for Lesson 3 on MCP architecture (hosts, clients, servers).
Reference
| AI apps (M) | Tools (N) | Custom integrations without MCP (M x N) | Connections with MCP (M + N) |
|---|---|---|---|
| 3 | 3 | 9 | 6 |
| 3 | 4 | 12 | 7 |
| 4 | 4 | 16 | 8 |
| 5 | 10 | 50 | 15 |
| Key fact | Detail |
|---|---|
| Created by | Anthropic engineers including David Soria Parra and Justin Spahr-Summers |
| Inspired by | Language Server Protocol (LSP), which solved the same M x N problem for code editors and language analyzers |
| Internal development began | Mid-2024 |
| Released as open standard | November 25, 2024 |
| Community adoption | Over 1,400 public MCP servers built, growing faster than Zapier's integration catalog did in its first five years |
Common errors and fixes
| Problem before MCP | What it caused | How MCP fixes it |
|---|---|---|
| Every tool used a different custom setup ("signal") | Developers had to learn and build a new connection for every app-tool pair | One shared protocol that every AI app and tool speaks |
| A tool's API or interface changed | Every AI app connected to that tool broke and needed a manual fix | Change the connection once; it syncs everywhere through the standard |
| Vendors built proprietary, locked-in connectors | Switching AI providers meant rebuilding all integrations from scratch | An open standard that any AI app or tool can join |
| Tools could run actions with no guardrails | Developers spent time building safety checks instead of improving AI systems | Tools must declare what they can do; nothing runs without approval |
Read the full walkthrough
The complete lesson, with screenshots and any downloads, is published on Substack as part of MCP Masterclass: Connect AI to Everything.
More in this section
- Lesson 1: What is MCP - Model Context Protocol?
- Lesson 3: How MCP Actually Works: Hosts, Clients, and Servers
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