MCP servers: what they are and why they matter

In one line: MCP (Model Context Protocol) is a USB port for AI agents. Any tool, database or API can be "plugged" into Claude or another agent via MCP, and the agent starts using it immediately.

The problem MCP solves

Before MCP, every tool integration was written by hand: custom function, custom parameter format, custom invocation logic. Want to hook up Notion? Write an integration. Want to connect your own DB? Write another one. Every agent had its own ecosystem.

MCP standardized this. Now:

The tool implements the MCP protocol once
Any MCP-compatible agent (Claude, ChatGPT, your own) connects in seconds
The agent automatically understands what the tool can do

How it works

An MCP server is a regular process (Python, Node, Go — anything) that talks to the agent over stdio or HTTP. It declares three things:

Tools — functions the agent can call (search_db, send_email)
Resources — data the agent can read (files, records)
Prompts — ready-made templates the agent can apply

When the agent wants to do something, it:

Queries the list of available tools
Picks the right one
Passes parameters
Gets the result

That's it.

Real MCP servers in the wild

filesystem — read/write files (built into Claude Code)
postgres — direct SQL queries
github — repos, issues, PRs
slack — send messages, read channels
brave-search — web search

Each one is a standalone process, each one is added to an agent's config with a single line.

Why this matters for you

If you're building a product for developers — add an MCP server. Any Claude Code user can plug in your tool in 30 seconds. That's a zero barrier to entry to millions of agents.

If you're building internal tools — MCP makes them agent-accessible without a rewrite. One business logic, one interface for all AI.

What's next

Upcoming posts will cover:

How to write your own MCP server in 50 lines
Security patterns (an MCP server is an attack surface)
Monetization via x402

Want an MCP server for your use case? Let's talk.