What is an MCP server? A clear explainer for developers
The Model Context Protocol explained without the jargon: what an MCP server is, the client-server model, tools vs resources vs prompts, and why it exists.
Read the explainer →How to build an MCP server in Python (production guide)
A working guide built around a real, open-source read-only Shopify server. Tools, annotations, the safety boundary tutorials skip, and the five mistakes worth avoiding.
Read the guide →MCP Inspector: how to debug and test your MCP server
The official MCP debugging tool, end to end. How to run it, what each panel does, and the silent failures it catches before a client ever sees them.
Read the guide →How to build an MCP server in TypeScript (the right way)
A minimal MCP server with the official TypeScript SDK, using the current stable API and the zod schema shape that trips most people up.
Read the guide →MCP vs function calling: what's the difference?
They're not competitors. Function calling is the model deciding when to use a tool; MCP is the standard for how tools are delivered. Here's how they fit together.
Read the explainer →The Claude Agent SDK: build your own agent
The same engine that powers Claude Code, driven from your own program. What the SDK gives you in production, how tools and MCP servers plug in, and when to reach for it.
Read the guide →Claude Code hooks: a practical guide
Hooks are shell commands that run at lifecycle events. The settings.json schema, exit-code behavior, a real guard example, and the gotchas worth knowing.
Read the guide →Claude Code subagents: how and when to use them
Subagents get their own context window and tool scope. How to define one, why the separate context matters, and the honest case for when not to bother.
Read the guide →Claude Code vs Cursor: an honest 2026 comparison
Two strong tools that have grown into each other's lanes. Where each genuinely wins, the real pricing, and which one to reach for when.
Read the comparison →AI agents for SEO: how we run 5,000+ tasks a month
What SEO work AI agents can actually do, what still needs a human, and the real numbers from running 5,220 agent tasks at 98% success for an agency.
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