docker run -p 8000:8000 ghcr.io/foundrynet/forge-sandbox

curl -X POST http://localhost:8000/v1/normalize \
  -H "Content-Type: application/json" \
  -d '{"oem": "haas", "data": {"S SPEED (RPM)": 8500, "SP_LOAD_PCT (%)": 84.7, "COOL_TEMP [°F]": 161.8}}'

{
  "normalized": {
    "spindle_speed_rpm": 8500,
    "spindle_load_pct": 84.7,
    "sensor_readings.coolant_temp": 72.1111
  },
  "coverage_pct": 100.0,
  "fields_total": 3,
  "fields_distinct_canonical": 3,
  "unit_conversions": [
    {"raw_field": "COOL_TEMP [°F]", "canonical_field": "sensor_readings.coolant_temp",
     "from": "f", "to": "c", "conversion": "fahrenheit_to_celsius",
     "raw_value": 161.8, "converted_value": 72.1111}
  ],
  "oem": "haas",
  "vertical": "cnc",
  "simulated": true
}

# 1. See what's here
curl -s localhost:8000/v1/machines | jq '.machines[].description'

# 2. Pull a raw reading — vendor tags, unnormalized
curl -s localhost:8000/v1/simulate/siemens | jq .data

{
  "Betriebszustand": "AUTOMATIK",
  "PROGRAMM": "WELLE_STUFE3.MPF",
  "Nist_Spindle (RPM)": 1203,
  "SPINDEL_AUSLASTUNG (%)": 62.4,
  "Kuehlmittel Temp (C)": 30.6,
  "STUECKZAHL (pcs)": 842,
  "Betriebsstunden": 14203.5
}

# 3. Normalize it
curl -s localhost:8000/v1/simulate/siemens \
  | jq '{oem, data}' \
  | curl -s -X POST localhost:8000/v1/normalize -H 'Content-Type: application/json' -d @- \
  | jq .normalized

{
  "execution_state": "AUTOMATIK",
  "program_name": "WELLE_STUFE3.MPF",
  "spindle_speed_rpm": 1203,
  "spindle_load_pct": 62.4,
  "sensor_readings.coolant_temp": 30.6,
  "part_count": 842,
  "operating_hours": 14203.5
}

# 4. Forecast — grab a series, ask whether it breaches
curl -s 'localhost:8000/v1/simulate/fanuc/series?field=MOTOR_TEMP&points=48' > /tmp/s.json

jq '{time_series: .values, threshold: 75.0, canonical_field: .canonical_field}' /tmp/s.json \
  | curl -s -X POST localhost:8000/v1/predict_breach -H 'Content-Type: application/json' -d @- \
  | jq '{will_breach, estimated_steps_to_breach, confidence, breach_window}'

{
  "mcpServers": {
    "forge-sandbox": {
      "type": "http",
      "url": "http://localhost:8000/mcp"
    }
  }
}

claude mcp add --scope user --transport http forge-sandbox http://localhost:8000/mcp

claude mcp remove forge-sandbox -s user

docker compose up --build          # build and run your local changes
docker build --target test .       # run the suite inside the shipping image

python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements-dev.txt
python -m pytest tests/ -q
uvicorn app.main:app --reload --port 8000

python3 tools/build_packs.py

forge-sandbox/
  app/
    main.py        FastAPI surface — the production response envelopes
    corpus.py      tag → canonical resolution, unit conversion, collisions
    simulate.py    the five machines
    predict.py     deterministic forecasting, production's response contract
    mcp_tools.py   MCP server, production tool descriptions
    packs/         generated mapping packs + the canonical dictionary
  tools/
    build_packs.py regenerates packs from the public sources
  tests/
    test_sandbox.py

- BASE_URL = "http://localhost:8000"
- headers = {}
+ BASE_URL = "https://forge.foundrynet.io"
+ headers = {"Authorization": f"Bearer {FORGE_API_KEY}"}

Sandbox:     fake data, real schema
Production:  real data, real schema, real predictions

Upgrade:     foundrynet.io