Articles, tutorials, and updates about the AI tools ecosystem.
Connecting your AI assistant to API testing tools through MCP lets you explore, test, and debug APIs conversationally. It's surprisingly useful for understanding unfamiliar APIs quickly.
Teams that integrate AI tools effectively organize differently than traditional dev teams. The shift isn't about replacing people. It's about restructuring workflows around human-AI collaboration.
Traditional product reviews don't translate well to AI tools. The tools are free, the quality is variable by run, and the review infrastructure barely exists. This creates information gaps.
MIT, Apache 2.0, GPL, AGPL: the license on an MCP server determines what you can legally do with it. For most developers this doesn't matter. For organizations, it can be a blocker.
Connecting your AI assistant to Notion, Obsidian, or other knowledge bases through MCP servers turns conversation into searchable organizational memory. Here's what's available and what works.
Agent tasks can consume anywhere from 5,000 to 500,000 tokens depending on complexity. Estimating costs before you start prevents unpleasant surprises on your API bill.
Directories curate. Registries host. The distinction matters because it affects what quality signals are available and how much you can trust a listing.
When an MCP server's maintainer moves on, the server doesn't disappear. It sits in registries collecting vulnerabilities. Understanding the lifecycle of abandoned tools helps you avoid them.
A walkthrough of using Skillful.sh's search, filtering, and comparison features to find MCP servers that match your specific needs, fast.
Larger context windows let AI models hold more tool results in memory during a task. But bigger isn't always better, and the relationship between context size and tool performance is nuanced.
Most developers add MCP servers over time and never review what they've accumulated. A periodic audit of your connected servers catches security risks and clutter before they become problems.
When an MCP server returns query results containing customer emails, financial data, or credentials, that data enters the AI model's context. Managing what goes in and what stays out requires deliberate design.
MCP has two sides: clients and servers. Confusing them leads to misunderstandings about what MCP is and how it works. Here's the distinction that makes everything else click.
Your MCP server's README is its storefront, documentation, and first impression rolled into one. Developers spend less than 60 seconds on it before deciding to install or move on.
The most useful MCP servers aren't the flashy ones. They're the boring ones that handle routine tasks you do fifty times a day. Start with those and you'll actually stick with the workflow.
Free, freemium, usage-based, enterprise tiers. The pricing landscape for AI tools is fragmented and changing fast. Understanding the models helps you budget realistically.
A security grade of B sounds better than C, but what's actually different between them? Here's what each grade level represents and how to interpret them for your decisions.
The description you write for your MCP server's tools directly determines whether AI models use them correctly. Most descriptions are too vague. Here's how to write ones that work.
That MCP server you installed three months ago and forgot about? It might have accumulated vulnerabilities, lost compatibility, or fallen behind on features. Neglect has a real price.
You've connected five MCP servers, but your AI assistant only seems to use two of them. The others sit idle even when they'd be helpful. Here's why that happens and how to fix it.