# Context Engineering > ๐Ÿง  Stop building AI that forgets. Master MCP (Model Context Protocol) with production-ready semantic memory, hybrid RAG, and the WARNERCO Schematica teaching app. FastMCP + LangGraph + Vector/Graph stores. Your AI assistant's long-term memory starts here. - **Type:** MCP server - **Install:** `agentstack add mcp-timothywarner-org-context-engineering` - **Verified:** Yes โ€” security-reviewed for prompt injection and unsafe behavior - **Seller:** [timothywarner-org](https://agentstack.voostack.com/s/timothywarner-org) - **Installs:** 0 - **Category:** [Databases](https://agentstack.voostack.com/c/databases) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [timothywarner-org](https://github.com/timothywarner-org) - **Source:** https://github.com/timothywarner-org/context-engineering - **Website:** https://techtrainertim.com ## Install ```sh agentstack add mcp-timothywarner-org-context-engineering ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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`) โ€” `onnxruntime` (a chromadb dependency) does not yet ship 3.14 wheels - Node.js 20+ (for Lab 01 and MCP Inspector) - [uv](https://docs.astral.sh/uv/) package manager (recommended for Python) - Claude Desktop or Claude Code ### Option 1: Hello MCP Lab (Beginner Entry Point) ```bash 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 1.5: MCP Chat CLI Lab (Python Bridge) ```powershell cd labs/lab-02-mcp-chat .\run.ps1 ``` `run.ps1` is the **on-rails launcher**: it bootstraps `.env`, lifts `ANTHROPIC_API_KEY` from the repo-root `.env`, and launches the chat REPL. **Lab 02** is the smallest complete Python **MCP client + stdio server + REPL** in the repo - the conceptual bridge between Lab 01 (JS hello-world) and WARNERCO (production-shaped). ### Option 2: WARNERCO Schematica (Flagship Teaching App) ```bash 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) | Segment | Topic | Focus | | ------- | ------------------------------ | --------------------------------------------------------------------------- | | **1** | Context, Made Visible | Consumer LLMs hide context; dev tools (Claude Code, Copilot) expose it | | **2** | MCP: Standard for Context | The protocol + a server on the official `mcp` SDK; current best practices | | **3** | Memory Tiers in a Real App | WARNERCO's four CoALA tiers live; orchestration kept under the hood | | **4** | MCP in the Tools You Use | Claude Code + GitHub Copilot in VS Code as context-engineering surfaces | Full plan: `instructor/course-plan-june-2026.md` (context-first, official-SDK-first; delivered 2026-06-30). The earlier orchestration-forward plan is archived under `instructor/archive/`. ### Segment Notebooks `notebooks/segment-1.ipynb` through `segment-4.ipynb` are four **prepopulated, verified teaching notebooks**, one per 50-minute segment. Each opens with a **repo-root anchor cell** that walks up to `.git`, so it runs from any working directory. They print **live counts** (never hardcoded), and Segment 3 runs the full pipeline with live Anthropic prose. Open them in **VS Code with the Jupyter extension** and **Run All**. Verified to run error-free from both the repo root and `notebooks/`. --- ## 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) | The **`reason` node** (LangGraph node 6) synthesizes prose via the official **`anthropic` Python SDK** (`AsyncAnthropic`); if the backend `.env` lacks the key it degrades gracefully - retrieval still works and the prose shows a fallback message. Episodic recall uses **Park et al.'s scoring formula** โ€” `ฮฑ_recency ยท 0.5^(hours/half_life) + ฮฑ_importance ยท stored + ฮฑ_relevance ยท cosine(query, summary)` โ€” 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**, **12 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)` โ€” keyword discovery with detail levels `name`, `summary`, `full` - `warn_describe_tool(name)` โ€” 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/ โ”œโ”€โ”€ src/warnerco/backend/ # WARNERCO Schematica (FastAPI + FastMCP + LangGraph) โ”œโ”€โ”€ labs/lab-01-hello-mcp/ # Hands-on beginner lab (JS) โ”œโ”€โ”€ labs/lab-02-mcp-chat/ # MCP client + server + chat REPL (Python, vendored) โ”œโ”€โ”€ notebooks/ # Four verified segment teaching notebooks (segment-1..4) โ”œโ”€โ”€ docs/ # Student materials, tutorials, diagrams โ”œโ”€โ”€ instructor/ # Instructor-only materials โ”œโ”€โ”€ config/ # Sample MCP client configurations โ”œโ”€โ”€ .vscode/ # VS Code workspace configuration โ”œโ”€โ”€ .claude/ # Claude Code agents and skills โ””โ”€โ”€ CLAUDE.md # Development instructions (SOURCE OF TRUTH) ``` **For development details, see [CLAUDE.md](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](docs/tutorials/coala-explainer.md) โ€” what CoALA is and where each tier lives in the WARNERCO codebase. **For the classroom demo:** see [docs/tutorials/coala-memory-walkthrough.md](docs/tutorials/coala-memory-walkthrough.md) โ€” 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` ```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 a single `oreilly-warnerco-schematica` entry (`MEMORY_BACKEND=chroma`) plus the GitHub Copilot remote MCP server. To list servers in VS Code: `Cmd/Ctrl+Shift+P โ†’ MCP: List Servers`. --- ## Testing with MCP Inspector ```bash npx @modelcontextprotocol/inspector uv run warnerco-mcp # Opens http://localhost:5173 ``` --- ## Resources - **[MCP Specification](https://spec.modelcontextprotocol.io/)** - Official protocol documentation - **[FastMCP Documentation](https://github.com/jlowin/fastmcp)** - Python MCP framework - **[CLAUDE.md](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](https://techtrainertim.com) - GitHub: [@timothywarner](https://github.com/timothywarner) - LinkedIn: [linkedin.com/in/timothywarner](https://www.linkedin.com/in/timothywarner/) --- ## License MIT License - 2026 Timothy Warner --- **Now go build AI systems that actually remember!** ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source โ€” we do not rehost the code. - **Author:** [timothywarner-org](https://github.com/timothywarner-org) - **Source:** [timothywarner-org/context-engineering](https://github.com/timothywarner-org/context-engineering) - **License:** MIT - **Homepage:** https://techtrainertim.com Install and usage instructions live in the source repository linked above. ## Pricing - **Free** โ€” Free ## Security capabilities Automated source analysis of v0.1.0 โ€” what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** yes - **Dynamic code execution:** no *"Yes" means the capability is present in the source โ€” more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** โ€” security scan: passed โ€” Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-timothywarner-org-context-engineering - Seller: https://agentstack.voostack.com/s/timothywarner-org - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack โ€” the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.