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.
- Python 3.11+ (3.12+ recommended for WARNERCO Schematica)
- Node.js 20+ (for Lab 01 and MCP Inspector)
- uv package manager (recommended for Python)
- Claude Desktop or Claude Code
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.jscd 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| 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 | Semantic Memory Stores | JSON, ChromaDB, Azure AI Search, Graph Memory, Scratchpad |
| 4 | MCP in Production | Claude Desktop, Claude Code, VS Code, GitHub Copilot, LangGraph |
The flagship teaching application demonstrates production MCP patterns with a 7-node hybrid RAG pipeline:
+---------------------------------------------------------------+
| FastAPI + FastMCP |
+---------------------------------------------------------------+
| LangGraph Flow (7-node Hybrid RAG) |
| parse_intent -> query_graph -> inject_scratchpad -> retrieve |
| -> compress -> reason -> respond |
+---------------------------------------------------------------+
| Hybrid Memory Layer |
| +-------------------+ +-------------------+ +-------------+ |
| | Vector Store | | Graph Store | | Scratchpad | |
| | JSON -> Chroma -> | | SQLite + NetworkX | | In-memory | |
| | Azure AI Search | | (Knowledge Graph) | | (Session) | |
| +-------------------+ +-------------------+ +-------------+ |
+---------------------------------------------------------------+
| Feature | JSON | ChromaDB | Azure AI Search | Graph | Scratchpad |
|---|---|---|---|---|---|
| Semantic Search | No | Yes | Yes | No | No |
| Relationship Queries | No | No | No | Yes | No |
| Session Memory | No | No | No | No | Yes |
| Best For | Prototyping | Local dev | Production | Connections | Working memory |
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
- API endpoint documentation
- Environment variable configuration
- LangGraph pipeline details
- Graph and Scratchpad Memory features
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"
}
}
}See .vscode/mcp.json in the repository for local and Azure APIM configurations.
npx @modelcontextprotocol/inspector uv run warnerco-mcp
# Opens http://localhost:5173- MCP Specification - Official protocol documentation
- FastMCP Documentation - Python MCP framework
- CLAUDE.md - Development instructions for this repository
Microsoft MVP - Azure AI and Cloud/Datacenter Management Microsoft Certified Trainer (25+ years)
- Website: techtrainertim.com
- GitHub: @timothywarner
- LinkedIn: linkedin.com/in/timothywarner
MIT License - 2026 Timothy Warner
Now go build AI systems that actually remember!
