You’ve built an MCP server. It accesses data, performs actions, and works perfectly in your local development environment.
Now what?
To make your tools truly useful, they need to be accessible—whether by your team, your organization, or the global community of AI developers. This guide covers how to take your MCP server from localhost to production, and how to register it so it can be discovered.
AI agents are getting smarter — and more dangerous.
Not because they reason better, but because they act without boundaries.
Agent Skills exist to fix that.
In the rapidly evolving landscape of AI, Skills have emerged as a transformative force, enabling developers to extend the capabilities of AI models far beyond their native functions. Whether you're building chatbots, virtual assistants, or complex AI-driven applications, mastering Skills is essential to unlocking new levels of performance and user engagement.
The "Hello World" phase of the Model Context Protocol is over.
As enterprises move from experimental chatbots to production-grade agentic systems, they are hitting the invisible walls of scale: token bloat, latency, governance, and discovery. What works for ten tools fails catastrophically at ten thousand.
Your MCP just became a memory hog. And it’s quietly burning your budget.
If your Model Context Protocol (MCP) catalog is growing into the hundreds—or thousands—of tools, you’re already facing the next invisible scalability wall: token bloat.
And it’s not a theory anymore.
What's the lesson from Brian Casel's experience about the future of SDKs, no-code workflows, and automation?
In short: SDKs are dead - well, at least for many use cases in modern application development.
Transcripts from podcasts are treasure troves of insights, ideas, and stories. However, accessing and utilizing this wealth of information can be challenging for marketers, product managers, and content creators—especially those without deep technical expertise.
Lenny Rachitsky posted on LinkedIn about sharing transcripts of his podcast episodes, I decided to format the transcripts for easy consumption by language models and AI tools.
In the rapidly evolving landscape of the Model Context Protocol (MCP), architects are facing a critical decision: how to secure and govern connections between AI clients and a sprawling ecosystem of tools and data servers.
The traditional answer is a Gateway—a centralized proxy that inspects every byte. The modern answer is a Connect Authority—a distributed Zero Trust model that separates permission from traffic.
AI is everywhere. Too everywhere. From answering "hi" to deciding how your production platform runs sanity checks, we've quietly crossed a line. Not because AI can't do these things, but because it shouldn't do all of them.
You want your API to be instantly usable inside AI assistants like ChatGPT, and Web UIs. The OpenAI UI SDK plus the Model Context Protocol (MCP) lets you expose your API as structured tools that large language models can call directly.
