So Here’s the Problem With AI Right Now
AI assistants like Claude are genuinely impressive. They can analyze things, write things, explain things. But they’re kind of stuck in their own little world. They can only work with what you actually give them.
Want Claude to help manage your servers? You’re going to be copy-pasting terminal output back and forth like it’s 1995. Want it to process a PDF? Upload it, wait, download the result, repeat for each document. The friction adds up fast, and honestly? It makes a lot of potentially useful workflows completely impractical.
The Model Context Protocol (MCP) exists to solve this - a standard way for AI assistants to connect directly with external tools and services. And I’ve been building a lot of those bridges.
Building the Bridges
MCP servers are basically the middleware between AI and everything else. When Claude needs to do something in the real world - check a server status, extract data from a document, automate a web browser - an MCP server handles that connection.
I’ve built over fifteen of these servers, each one solving a specific integration problem:
Document processing. Browser automation. Cloud infrastructure management. Domain and DNS control. Electronics design tools. Android device automation. Each server exposes a clean interface that Claude can use naturally, like these capabilities were built in from the start.
It’s one of those situations where once you start building these things, you keep finding more problems they can solve.
Why I Care About Standards
Here’s something that’s been on my mind a lot lately. The web took thirty years to get security mostly right. Three decades of patches, breaches, and retrofitted protections. We’re STILL dealing with that technical debt.
MCP offers something different: a chance to get the architecture right from the beginning. Instead of building custom integrations between every AI and every tool (the dreaded N times M problem), MCP provides a standard protocol. Build once, connect to everything. Self-describing servers that announce their capabilities. Context built in from the start, not bolted on later.
The ecosystem reached 5,000+ servers in eight months. What HTTP took decades to achieve, MCP accomplished in under a year. That’s what happens when you design for the actual problem instead of around it.
Everything’s Open Source
Everything I’ve built here is open source. Sixteen packages on PyPI. Contributions to the FastMCP framework (which is at 21,000+ stars now - pretty wild). Patterns and practices shared with the community.
When you use these tools daily, you find ways to make them better. Pull requests, documentation, examples. The ecosystem grows because people contribute back what they’ve learned. I’ve been doing this long enough to know that’s how good tools actually get built.
What This Actually Enables
The MCP servers I’ve built handle real workloads. Sub-150-millisecond response times. Twenty-five tool calls per second. Fifty concurrent connections. This isn’t proof-of-concept stuff - it’s production reliability for production use.
But honestly, the numbers aren’t even the point. The point is what becomes possible when AI can actually DO things instead of just talking about doing things. Complex workflows that used to require constant human babysitting become smooth, automated processes. The AI stops being just a conversation partner and becomes a genuine collaborator.
That shift - from “AI as chat buddy” to “AI as capable assistant” - that’s what MCP enables.
What Gets Me Excited About This
New protocols succeed when they solve real problems better than existing solutions. MCP isn’t interesting because it’s new technology - it’s interesting because it makes AI genuinely useful for tasks that were completely impractical before.
The work I’ve done on MCP servers is some of the most technically interesting stuff I’ve worked on in a while. Forty years of computing experience and I’m still finding new problems that excite me. But the real satisfaction comes from watching people use these tools to accomplish things they couldn’t before. That’s always been the point - building what doesn’t exist yet, so others can do work that wasn’t possible before.
What’s next? I have ideas…
