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

Episodic recall uses Park et al.'s scoring formula — α_recency · 0.5^(hours/half_life) + α_importance · stored + α_relevance · cosine(query, summary) — and warn_episodic_recall returns the per-event score breakdown so students can see why each memory surfaced.

The knowledge graph is indexed at src/warnerco/backend/data/graph/knowledge.db with 117 entities and 221 relationships across 6 predicates (has_tag, compatible_with, belongs_to_model, has_status, has_category, contains).

Progressive Tool Loading

The server registers 28 MCP tools, 11 resources, and 5 prompts. Two meta-discovery tools implement progressive tool loading per Anthropic's "code execution with MCP" guidance:

• warn_search_tools(query, detail, limit) — keyword discovery with detail levels name, summary, full
• warn_describe_tool(name) — full schema for a single tool by name

Both meta-tools self-exclude from warn_search_tools results, so count is up to 26 even when total is 28. Clients can list tools cheaply, then pull full schemas only for what they actually plan to call.

Repository Structure

context-engineering/
├── src/warnerco/backend/      # WARNERCO Schematica (FastAPI + FastMCP + LangGraph)
├── labs/lab-01-hello-mcp/     # Hands-on beginner lab
├── docs/                      # Student materials, tutorials, diagrams
├── instructor/                # Instructor-only materials
├── config/                    # Sample MCP client configurations
├── .vscode/                   # VS Code workspace configuration
├── .claude/                   # Claude Code agents and skills
└── CLAUDE.md                  # Development instructions (SOURCE OF TRUTH)

For development details, see CLAUDE.md - the source of truth for:

• Complete MCP tool reference (28 tools)
• API endpoint documentation
• Environment variable configuration (incl. EPISODIC_*)
• 9-node LangGraph pipeline details
• All four CoALA memory tiers (Working / Episodic / Semantic / Procedural)

For the framework explainer (read this first): see docs/tutorials/coala-explainer.md — what CoALA is and where each tier lives in the WARNERCO codebase.

For the classroom demo: see docs/tutorials/coala-memory-walkthrough.md — the ~4-minute four-tier classroom path.

MCP Client Configuration

Claude Desktop

Windows: %APPDATA%\Claude\claude_desktop_config.json Mac: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "warnerco": {
      "command": "uv",
      "args": ["run", "warnerco-mcp"],
      "cwd": "C:/github/context-engineering/src/warnerco/backend"
    }
  }
}

Claude Code (project scope)

.claude/mcp.json is checked in with two entries pointing at the same server: warnerco-schematica-claude and warnerco-coala-memory (the second pre-pins the episodic-memory env vars for class demos).

VS Code Copilot

.vscode/mcp.json is checked in with warnerco-schematica-vscode (basic) and warnerco-coala-memory (with dev.watch for hot-reload during class). To list servers in VS Code: Cmd/Ctrl+Shift+P → MCP: List Servers.

Testing with MCP Inspector

npx @modelcontextprotocol/inspector uv run warnerco-mcp
# Opens http://localhost:5173

Resources

• MCP Specification - Official protocol documentation
• FastMCP Documentation - Python MCP framework
• CLAUDE.md - Development instructions for this repository

Your Instructor

Tim Warner

Microsoft MVP - Azure AI and Cloud/Datacenter Management Microsoft Certified Trainer (25+ years)

• Website: techtrainertim.com
• GitHub: @timothywarner
• LinkedIn: linkedin.com/in/timothywarner

License

MIT License - 2026 Timothy Warner

Now go build AI systems that actually remember!