Why MCP Is the Quiet Revolution Nobody's Talking About
Model Context Protocol turns AI assistants into real workhorses.
MCP standardizes how AI clients connect to data sources and tools. It's been shipping for over a year. Here's why it matters more than the next benchmark bump.
Model Context Protocol is to AI assistants what HTTP was to the web - a boring, standardized protocol that unlocks composability.
The problem MCP solves
Before MCP, every AI client (Claude Desktop, Cursor, Continue, etc.) had a custom way to integrate with data sources and tools. If you wrote a Postgres adapter for one, it didn't work on another. The integrations didn't compose.
MCP is the cable. You build an MCP server once (for your database, your codebase, your internal docs, your Jira, whatever) and any MCP-compatible client can use it.
What I built
I run a personal MCP server with three things plugged in:
- My Linear workspace
- My Notion (where my engagement notes live)
- A read-only adapter for the codebases I'm currently working on
When I'm in Claude Desktop, I can ask "what's blocked on the X engagement" and get a real answer pulled from across all three. When I'm in Cursor, the same servers are available - different client, same data.
Where it's going
The most interesting MCP traffic right now is tools, not data. Servers that can run code, query APIs, send Slack messages. The ecosystem is thin but growing fast.
For client work I now treat "is there an MCP for this?" as the first question. If yes, integration is hours. If no, it's days.
The boring power
Most revolutionary technologies arrive as boring protocols. MCP is the protocol layer for the agent era. Worth your attention even if you're not building AI yourself.