How I Turned 1,079 GitHub API Endpoints into 25 AI-Ready Tools

If you've tried connecting an AI assistant to a REST API through MCP (Model Context Protocol), you've probably hit the same wall I did: the tooling either doesn't exist for the API you need, or it generates hundreds of tools that make the LLM choke. I built MCPForge to fix that. It's a CLI that takes any OpenAPI spec and generates a production-ready MCP server, with an AI optimization layer that curates endpoints down to the ones that actually matter. The Problem MCP is blowing up. Claude Desktop, Cursor, and a growing list of AI tools support it. But if you want to connect one of these tools to a REST API, you have two options: Option 1: Write an MCP server by hand. Define every tool, write HTTP handlers, wire up auth, handle errors, write a README. Hours of boilerplate per API. Option 2: Auto-generate from the OpenAPI spec. Tools like FastMCP and Stainless can do this. But the output is a 1:1 mapping of endpoints to tools. A big API like GitHub has over 1,000 endpoints. Dumping 1,000