Building custom tools for your AI assistant
Your AI assistant can only do what it has tools for. MCP lets you build new tools so it can interact with any system you need.
Out of the box, an AI coding assistant can read files, edit files, and run commands. But what if you need it to check your website, query a database, or interact with a design tool? That's where MCP (Model Context Protocol) comes in. It lets you build custom tools that extend what your assistant can do.
Think of it like giving your assistant new abilities. Without MCP, it can only work with files on your computer. With an MCP tool for your database, it can look up data. With one for your website, it can take screenshots and check for errors. Each tool you build opens up a new capability.
Start with one simple tool for something you do repeatedly. Maybe you check your website after every deploy. Build a tool that takes a screenshot of any URL. Now your assistant can do that check for you.
The key is starting small. One tool that does one thing well is better than ten tools that sort of work. Get the first one solid, then build more as you find new needs.
When you find yourself doing the same manual step over and over during your workflow. 'Check the website after deploy.' 'Look up this data in the database.' 'Post an update to the team channel.' Each of those is a tool waiting to be built.
Product leader shipping across enterprise SaaS, AI in production, and 0→1. Writing about what actually ships — not what sounds good in a deck.