Anthropic's MCP Launch: Five Takeaways from a Skeptic

When Anthropic announced the Model Context Protocol in late 2024, I thought it was a Claude-specific play. A year of using it for client work changed my mind.

When Anthropic announced MCP in late 2024, I read the blog post and thought "neat, but proprietary." A year later I'm running it daily.

What I got wrong

I thought MCP would stay Anthropic-only. It hasn't. Major IDE-class tools have adopted it, dozens of community servers exist, and the protocol itself is open.

I thought "tools as APIs" was already a solved problem. It wasn't. Every AI client had a different way of integrating tools, and integrations didn't compose. MCP fixed that.

I thought the abstraction overhead would matter. It doesn't. The protocol is thin enough that it doesn't get in the way.

Five takeaways

1. MCP is the USB-C moment for AI tooling. Boring, standard, plug-anywhere.
2. Build MCP servers, not bespoke integrations. Future-proofs your tooling investment.
3. The ecosystem will fragment around quality, not protocol. The good MCP servers (well-tested, well-documented, well-scoped) will pull ahead.
4. Auth is still rough. Most production MCP servers I've shipped need bespoke auth wrappers. The spec is improving here, but not done.
5. It changes how I architect agents. Instead of one big agent with bespoke tools, I now build small agents that compose MCP-provided capabilities.

What I run

• A personal MCP server for my Linear + Notion + Cal.com
• Filesystem MCP for code review
• A Postgres read-only MCP for client work where I need to ask data questions naturally
• The official GitHub MCP for repo operations

That's my agent toolkit. Composable. Portable across clients.

Where it goes

The MCP-as-protocol gives us a chance to build real interoperability between AI assistants. We've never had that before. Worth investing your time understanding.

References

• MCP announcement
• MCP spec