User: "Why was sample 42 classified as degraded?"

Copilot: The model classified this sample as degraded (probability: 0.91)
         primarily because of 3 factors: total_acid_number = 4.8 (pushing toward
         the positive class by +0.28), water_content_ppm = 312.0 (+0.19),
         and viscosity_40c = 48.2 (+0.14). The top opposing factor is
         flash_point = 215.0 (pushing away from the positive class by -0.06).

# 1. Install dependencies
uv sync

# 2. Train the models (run once)
uv run python scripts/train_lubricant_model.py

# 3. Run the test suite (should show 100+ passing)
uv run python -m pytest tests/ -v

# 4. Open VS Code — the MCP server starts automatically via .vscode/mcp.json
#    Open Copilot chat in agent mode and ask:
#    "What models are available?"

VS Code Copilot (Sonnet 4.5)
    │  natural language question
    ▼
MCP Client (built into Copilot)
    │  JSON-RPC over stdio
    ▼
xai-toolkit MCP Server  (server.py — thin adapter only)
    │
    ├── explainers.py   SHAP values, PDP/ICE, global importance, data hashing
    ├── narrators.py    Structured data → deterministic English paragraphs
    ├── plots.py        matplotlib → base64 PNG (bar, waterfall, PDP+ICE)
    ├── schemas.py      Pydantic contracts — single source of truth
    └── registry.py     ModelRegistry — load and serve multiple model types

xai-mcp/
├── src/xai_toolkit/         # Source package
├── tests/                   # 100+ pytest tests
├── docs/
│   ├── decisions/           # 7 Architecture Decision Records
│   └── scalability-path.md  # PoC → Production roadmap
├── scenarios/               # YAML acceptance criteria (day1–day5)
├── scripts/                 # Model training scripts
├── models/                  # Trained model artifacts
├── data/                    # Test datasets
└── .vscode/mcp.json         # MCP server configuration

# Full test suite
uv run python -m pytest tests/ -v

# Write snapshot golden files (run once after first install)
uv run python -m pytest tests/test_snapshots.py --snapshot-update -v

# Just the fast unit tests (no model loading)
uv run python -m pytest tests/test_narrators.py tests/test_explainers.py tests/test_reproducibility.py -v

# Second model integration tests (requires trained RF model)
uv run python -m pytest tests/test_second_model.py -v