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.

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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.

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Course Structure (4 x 50 Minutes)

Segment	Topic	Focus
1	All About Context	Token economics, context loss types, why RAG isn't enough
2	All About MCP	FastMCP, FastAPI, tools, resources, prompts, elicitations
3	CoALA Four-Tier Memory	Working / Episodic / Semantic / Procedural — all four tiers in one app
4	MCP in Production	Claude Desktop, Claude Code, VS Code, GitHub Copilot, LangGraph

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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
Working	Session observations & inferences	data/scratchpad/notes.db (SQLite)	inject_scratchpad
Episodic	Timestamped events with importance	data/episodic/events.db (SQLite)	recall_episodes + log_episode
Semantic	Durable facts (incl. consolidated FACT-*)	Vector store (Chroma / Azure / JSON)	retrieve
Procedural	Versioned skills/workflows	MCP @mcp.prompt() registrations	(user-invoked, not in pipeline)

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.

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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.

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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.

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Testing with MCP Inspector

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

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Resources

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

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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

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License

MIT License - 2026 Timothy Warner

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Now go build AI systems that actually remember!