Build a Custom MCP Server with Python

Turn Your Agent’s Tools Into a Plug-Anything Server

You built an agent whose tools lived inside its own code. But the tools people actually reach for — in Claude Desktop, Claude Code, or their own apps — plug in over one open standard: the Model Context Protocol (MCP). Write a tool once as an MCP server, and any AI client can use it.

In this course you build opsmcp: you take opsbot’s powers — a crypto price, runbook search, live system checks, and a guarded restart — and expose them over MCP. You start from the bare wire, hand-writing a JSON-RPC server so you see exactly how MCP works, then rebuild it properly with the official Python SDK (FastMCP) — with tools, resources, and prompts, a real human-in-the-loop safety gate, and your own agent refactored into an MCP client.

What You’ll Build & Learn

• What MCP is and the M×N problem it solves — and the JSON-RPC wire, hand-written so it’s no mystery
• Build a real server with FastMCP: tools, resources (your runbooks), and prompts
• Drop your RAG search in as a tool, return structured output, and handle errors over the wire
• Enforce safety across the boundary: tool annotations, a server-side allowlist, and human confirmation via elicitation
• Refactor your agent into an MCP client, then register the same server in Claude Desktop / Claude Code
• Go remote over Streamable HTTP, then package it with a proper entry point and a safety review

How You’ll Learn

This is a build-on-your-machine project, not a lecture:

• Build one real server — opsmcp — module by module
• Lock in each concept with a quick quiz
• Download the complete, tested solution code for every step
• Use the free Mistral API and the MCP Inspector — no GPU, no paid tooling

Where This Fits Your Journey

This is the capstone of the BytExplorer AI-Assisted Developer path. It follows Building AI Agents with Python — it takes the very agent you built and turns its tools into a server the whole ecosystem can use. That’s the difference between an AI that helps you and one whose abilities plug into the tools everyone already uses.

Comfortable with Python and have built an agent (or ready to)? You’re set. By the end you’ll have built a custom MCP server, plugged it into Claude and your own agent, and learned to let an AI touch real systems — safely.

Ready to build a server the whole AI ecosystem can plug into? Jump in.