Articles, tutorials, and updates about the AI tools ecosystem.
Nobody loves writing API docs. AI skills can generate them from your code, keep them updated as endpoints change, and produce documentation that developers actually want to read.
MCP servers are infrastructure now, and infrastructure needs monitoring. Here's how to track server health, catch failures early, and keep your AI tool integrations reliable.
MCP servers connect your AI assistant to external systems, and those connections need testing just like any other integration. Here's a practical approach to testing MCP server setups.
AI agents can own more of your deployment pipeline than you'd expect. From running tests to deciding whether a release is safe to ship, here's how to build CD with agents in the loop.
Managing Docker containers through conversational AI saves time on routine tasks like restarting services, checking logs, and scaling replicas. Here's how MCP servers make that work.
The most popular MCP servers aren't always the best ones. And the best ones aren't always the most popular. Understanding why helps you avoid the trap of equating downloads with quality.
Working with spreadsheets through your AI assistant is one of those capabilities that sounds minor until you try it. Suddenly data exploration, cleaning, and analysis become conversational.
The stats dashboard shows ecosystem growth, category trends, and health metrics in real time. Here's how to read the charts and what the numbers actually tell you.
Most AI agents go to production with minimal testing. The testing tools and practices that work for traditional software don't transfer cleanly, and agent-specific testing is still immature.
If your product has an MCP server or AI integration, your pricing page should mention it. Most don't, and they're missing a discovery channel that converts well.
Users who depend on your MCP server need to know what changed between versions. A changelog isn't just documentation. It's trust infrastructure.
Individual bookmarks are useful. Shared collections are powerful. When teams curate and share tool collections, the evaluation work of each member benefits everyone else.
Connecting your AI assistant to monitoring tools like Datadog, Grafana, or CloudWatch lets you check system health conversationally. Ask about alerts, metrics, and incidents without switching dashboards.
Sometimes the best tool call is no tool call. AI assistants need to recognize when they can answer from knowledge alone versus when they need to reach for an MCP server.
Getting your MCP server, skill, or agent listed on Skillful.sh increases its visibility across the ecosystem. Here's how the submission process works and what makes a listing effective.
You've searched everywhere and there's no MCP server for what you need. Your options range from building one yourself to creative workarounds that avoid building altogether.
An MCP server with 3 tools that each do one thing perfectly tends to be more useful than one with 20 tools that each kinda work. The Unix philosophy applies here more than you'd expect.
Not every use case needs an A-grade tool. A personal side project and a production financial system have very different security thresholds. Matching the grade to the context saves time.
External APIs have rate limits. AI agents make many requests. When these two realities collide, the tools need strategies for staying within limits while still getting the job done.
More servers means more capability, right? Not exactly. Connecting too many MCP servers at once creates confusion, slows performance, and makes your AI assistant less effective, not more.