Building Your Own MCP Server for AI

Capstone

Build, deploy, connect, and test a production-style MCP server — a checklist you can reuse.

Ship one server that a second person could enable without a screen share. You may reuse httpbin, GitHub, or an API you already run. The grade is the checklist, not the brand of backend.

Build

  • Python 3.10+ venv, mcp[cli]>=2,<3 pinned, server.py (or a package) with MCPServer.
  • At least two tools that match jobs, not raw paths. Structured ok / error results.
  • At least one resource or one prompt — you choose which fits the product.
  • Secrets from the environment only. No tokens in git.
  • A README: what the tools do, how to run stdio, how to run HTTP.

Connect

  • A host config (Cursor or Claude Desktop) that starts stdio with the venv Python.
  • A recorded chat (screenshot or gist) where the model called a tool and used the result.

Deploy

  • Process manager on a VM or equivalent (container is fine).
  • HTTPS URL or a documented reason you stayed on stdio-only (laptop personal server).
  • If remote: bind locally, proxy, no open app port.

Test

  1. Happy path for each tool.
  2. Validation failure (empty required field) returns JSON, not a stack trace.
  3. Missing secret returns a clear ok: false.
  4. A destructive tool, if any, requires confirmation. If none, write that you omitted deletes on purpose.

Done

When every box is honest, you have a production-style MCP server: small surface, boring transport, secrets out of band. Start the next API from Build a Useful MCP Server, not from the OpenAPI file.

Course hub: Building Your Own MCP Server for AI.