I Was Hand-Writing Every AI Tool. Then I Discovered MCP Servers.

What tool calling and MCP actually mean, and how they fit together when you're building real AI products. I've been building Pulse, a voice AI co-pilot for engineering work that talks to Jira and GitHub. The idea is simple: speak a command, Claude figures out what to do, your project management tools respond. To make it work, I had to give Claude the ability to interact with Jira and GitHub. So I did what most people do when they start building with LLMs: I wrote the tools by hand. tools : [ { name : " create_jira_ticket " , description : " \" ... \" , input_schema: { ... } }, " { name : " get_jira_issue " , description : " \" ... \" , input_schema: { ... } }, " { name : " update_jira_status " , description : " \" ... \" , input_schema: { ... } }, " ] Three tools. Done. It worked fine. Then I learned what an MCP server actually is, and I realised I had been building with a teaspoon when a fire hose was sitting right there. First, what is tool calling? When you build an LLM application,