Most Model Context Protocol (MCP) tutorials stop at the "hello world" stage. They show you how to connect Claude to a mock SQLite database or fetch the weather, leaving you to figure out the hard part alone. When you try to build a real-world agent that handles rate limits, manages state across long-running sessions, or securely executes Python code in a sandboxed environment, those basic templates fall apart. You end up with a brittle system that works on localhost but crashes under the slightest production load.
The gap between a hobbyist script and a production-grade MCP server is massive. You need to handle asynchronous I/O, secure your transport layers, and configure Claude Code to actually write, test, and deploy code autonomously without breaking your git history.
This is why we built the "Production MCP & Claude Code in Russian" course. We bypass the surface-level theory and dive straight into building resilient, production-ready infrastructure. Across 12 intensive modules, we build real-world Python MCP servers from scratch. We focus on implementing robust error handling, managing complex context windows, and safely integrating Claude Code directly into your development workflow.
For example, instead of running insecure local commands, we configure a secure, sandboxed execution tool using FastMCP and Docker:
@mcp.tool()
async def run_secure_code(code: str) -> str:
container = client.containers.run(
"python:3.11-slim",
command=f"python -c {shlex.quote(code)}",
mem_limit="128m",
network_mode="none",
detach=True
)
return container.wait(timeout=5)
The course is recorded in Russian, designed for developers who want to master AI agents in Python. For $149 lifetime access, you get all 12 modules, plus you can join our Code Club for $49/month to get weekly code reviews, live architecture teardowns, and direct support.
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