Model Context Protocol (MCP)
MCP is an open protocol standardizing how AI apps connect to tools and data, so each integration is built once instead of once per app-tool pair.
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in 2024, that defines a common client-server interface for connecting AI applications to external tools, data sources, and prompts — so a tool built once as an MCP server works with any MCP-compatible application, instead of needing a custom integration for every pairing.
Before a shared protocol, connecting N AI applications to M external tools meant writing roughly N × M custom integrations — every app team reimplementing the same GitHub connector, the same database connector, the same Slack connector, each slightly differently. MCP turns that multiplication into addition: each application implements one MCP client, each tool implements one MCP server, and any client can talk to any server through the same interface.
How it works
An MCP server exposes its capabilities in three standard categories — tools (functions the model can call), resources (data it can read), and prompts (reusable templates) — over a standard transport, typically Streamable HTTP for remote servers or stdio for local ones. An MCP client, embedded in the AI application, discovers what a connected server offers and issues calls against that same schema regardless of which server it's talking to. Authentication and permissions are handled per connection, so a host application can connect to a dozen different MCP servers — a GitHub server, a database server, an internal ticketing server — without writing bespoke glue code for each.
Why it matters
The practical effect is that tool integrations become reusable infrastructure instead of one-off application code. A company that builds an internal MCP server for its ticketing system gets every MCP-compatible AI application as a client for free — the chat assistant, the coding agent, the support bot — without touching that server's code again. This is the same shift that a language server brought to code editors, or a standard connector brought to hardware: less code, more reuse, less duplicated maintenance whenever an underlying tool's API changes.
A worked example
Consider 6 AI applications that each need access to 9 external tools and data sources. Built as bespoke integrations, that's 6 × 9 = 54 separate connectors to write and maintain — every app-tool pair its own piece of code. Built on MCP, it's 6 clients plus 9 servers = 15 integration points total, a 3.6x reduction — and any new 10th tool now only needs one new server to become available to all 6 applications at once, rather than 6 new integrations.
How miatz teaches it
MCP shows up in the agents track right after learners have built a bare tool-calling loop by hand — so the pain MCP solves is visible first: learners wire up two or three tools with custom glue code, feel the duplication when a second agent needs the same tools, and only then swap in an MCP server to see the maintenance burden disappear. The comparison is deliberate: Miatz teaches infrastructure patterns by having learners hit the problem before handing them the fix.