🔌 MCP Module — Model Context Protocol¶

Architecture, server implementation, RAG integration, and production deployment

Overview¶

Model Context Protocol (MCP) is an open standard, introduced by Anthropic in November 2024 and since December 2025 governed under the Linux Foundation's Agentic AI Foundation, for connecting AI applications to tools, data and prompts. One protocol replaces an N×M matrix of custom integrations: write a server once and any MCP host can use it.

The current spec revision is 2026-07-28, which made the protocol stateless (no initialize handshake or sessions). Pages here call out where earlier revisions (2025-03-26, 2025-06-18, 2025-11-25) differ, because interviewers and most deployed servers still reference them.

Contents¶

#	Document	Description
1	MCP Fundamentals	Architecture, protocol mechanics, setup guide
2	MCP Interview Questions	Staff/Principal-level Q&A transcript
3	MCP Implementation & RAG	Building custom MCP servers, RAG integration
4	MCP Production Architecture	Production deployment, security, tradeoffs
5	MCP Explained (draw.io)	Step-by-step walkthrough with a diagramming example

Server Implementations¶

servers/ — MCP server code:
calculator_server.py — Simple math tools (tutorial)
database_server.py — PostgreSQL wrapper with rate limiting & auth
rag_server.py — RAG pipeline integration (uses ../rag/implementation/)
common/ — Shared utilities: rate limiter, circuit breaker, auth
clients/ — Python client and Claude Desktop config
tests/ — Pytest test suite

Quick Start¶

The code targets the official Python SDK v2 (mcp>=2, spec revision 2026-07-28).

cd ai-engineering/mcp/
pip install -r requirements.txt

# The client launches the stdio server itself; no need to start it first
python -m clients.python_client --server calculator --list

# Interactive debugging with the MCP Inspector
npx @modelcontextprotocol/inspector python -m servers.calculator_server

# Tests
python -m pytest tests/ -v