Context Engineering with MCP: Build AI Systems That Actually Remember

Welcome to the training hub for mastering Context Engineering with Model Context Protocol (MCP). This course teaches you to implement production-ready semantic memory systems for AI assistants using Python, FastAPI, FastMCP, and LangGraph.

Quick Start

Prerequisites

• Python 3.13 (pinned in .python-version) — onnxruntime (a chromadb dependency) does not yet ship 3.14 wheels
• Node.js 20+ (for Lab 01 and MCP Inspector)
• uv package manager (recommended for Python)
• Claude Desktop or Claude Code

Option 1: Hello MCP Lab (Beginner Entry Point)

git clone https://github.com/timothywarner-org/context-engineering.git
cd context-engineering/labs/lab-01-hello-mcp/starter
npm install && npm start

# Test with MCP Inspector (in another terminal)
npx @modelcontextprotocol/inspector node src/index.js

Option 2: WARNERCO Schematica (Flagship Teaching App)

cd src/warnerco/backend
uv sync
uv run uvicorn app.main:app --reload    # HTTP server at http://localhost:8000
uv run warnerco-mcp                      # MCP stdio server for Claude Desktop
uv run warnerco-restart                  # Force-kill port 8000 and restart uvicorn

The warnerco-restart command (from scripts/restart_server.py) terminates anything bound to port 8000 (Windows: netstat + taskkill /F /T; POSIX: lsof + SIGKILL) before restarting. Flags: --kill-only, --port N.

Course Structure (4 x 50 Minutes)

WARNERCO Schematica Architecture

The flagship teaching application exercises all four CoALA memory tiers (Sumers et al. 2024) in a 9-node LangGraph pipeline:

+--------------------------------------------------------------------------+
|                          FastAPI + FastMCP                               |
+--------------------------------------------------------------------------+
|  LangGraph Flow (9-node CoALA-tiered RAG)                                |
|  parse_intent -> query_graph -> inject_scratchpad -> recall_episodes ->  |
|  retrieve -> compress_context -> reason -> respond -> log_episode        |
+--------------------------------------------------------------------------+
|  Four CoALA Memory Tiers                                                 |
|  +------------+  +-----------+  +----------+  +------------------------+ |
|  | Working    |  | Episodic  |  | Semantic |  | Procedural             | |
|  | Scratchpad |  | events.db |  | Vector   |  | MCP Prompts (versioned)| |
|  | (SQLite)   |  | (SQLite)  |  | store    |  | catalog://procedural   | |
|  +------------+  +-----------+  +----------+  +------------------------+ |
+--------------------------------------------------------------------------+
|  Consolidation ("sleep cycle"): scratchpad+episodic --(ctx.sample)--> semantic |
+--------------------------------------------------------------------------+

CoALA Tier Reference

| Tier | What it stores | Backed by | LangGraph node | | ----------- | --------------------------------------------- | --------------------------------

... View full README on GitHub