Generate and Debug MCP Servers from Scratch

Scraped MCP Examples ghx tool

Building complex components like Model Context Protocol (MCP) servers from scratch can be challenging, especially without good reference implementations. This lesson demonstrates a powerful workflow combining the ghx CLI tool with AI assistants (like Google's AI Studio and Cursor's agent) to overcome this hurdle.

You'll learn how to:

1. Use ghx: Leverage the ghx command-line tool (which wraps the GitHub CLI) to search GitHub for real-world code examples based on specific import paths (e.g., modelcontextprotocol/sdk/server/mcp.js).
2. Refine Search Results: Use ghx options (--help, --limit, --max-filename, NOT demo) within Cursor's chat to filter results, exclude simple demos, and gather a rich set of relevant MCP server implementations.
3. Provide AI Context: Copy the aggregated code examples found by ghx into an AI environment like Google AI Studio (or ChatGPT).
4. Handle Context Limits: See techniques for trimming large context files (like using Cursor's Cmd+K inline edit with head -n commands) to fit within model limits while retaining valuable examples.
5. Generate Code: Prompt the AI (Gemini 2.5 Pro in AI Studio or ChatGPT O3) to generate a new MCP server (e.g., with five distinct tools) based solely on the provided real-world examples.
6. Integrate and Debug: Set up the generated TypeScript code (mcp_server.ts) locally in Cursor, install dependencies (pnpm install), and use Cursor's agent (Cmd+I) or inline fixes (Cmd+K) to resolve TypeScript errors and path issues.
7. Configure in Cursor: Add the newly created MCP server (running via bun mcp_server.ts) to Cursor's MCP settings, using the absolute path and the bun command.
8. Test the MCP Server: Interact with Cursor's chat agent to call the custom tools (e.g., add_note, list_notes, write_managed_file) defined in the AI-generated MCP server, verifying its functionality.

This lesson showcases how combining targeted code searching (ghx) with the generative power of AI, fueled by relevant examples, can dramatically accelerate the process of building and bootstrapping complex tools and applications.