samirsaci

"""
  HOW TO READ THIS RUN (based on the sample JSON)
    -----------------------------------------------
    - Objective = cost: the model opens 4 plants (INDIA-LOW, JAPAN-HIGH, BRAZIL-HIGH, INDIA-HIGH),
      heavily exporting from INDIA and BRAZIL to the USA, while JAPAN supplies itself.
    - Unit economics: unit_cost ≈ €116.10; total_costs ≈ €5.683M (divide by 1e6 for M€).
    - Market economics: “JAPAN” is the most expensive market; “INDIA” the cheapest.
    - Localization ratio: local_prod / total_prod = 18,050 / 48,950 ≈ 36.87% local, 63.13% export.
    - Footprint per unit: e.g., unit_co2 ≈ 35.55 kgCO₂e/unit. To approximate total CO₂:
        unit_co2 * total_units ≈ 35.55 * 48,950 ≈ 1,740,100 kgCO₂e (≈ 1,740 tCO₂e).QUICK SANITY CHECKS

samirsaci

"""
    OUTPUT (matches your service schema)
    ------------------------------------
    The service returns { "input_params": {...}, "output_results": {...} }.
    Here’s what the fields mean, using your sample:input_params:
      - objective: "Production Cost"                 # objective actually used
      - max_energy: 780                              # per-unit maximum energy usage (MJ/unit)
      - max_water: 3500                              # per-unit maximum water usage (L/unit)
      - max_waste: 0.78                              # per-unit maximum waste (kg/unit)
      - max_co2prod: 41                              # per-unit maximum CO₂ production (kgCO₂e/unit, production only)

samirsaci

"""
    INPUT (LaunchParamsNetwork)
    ---------------------------
    - objective: str (default "Production Cost")
        One of {"Production Cost", "CO2 Emissions", "Water Usage", "Energy Usage"}.
        Sets the optimization objective.
    - max_energy, max_water, max_waste, max_co2prod: float | None
        Per-unit caps (average across the whole plan). If omitted, service defaults
        from your config are used. Internally the model enforces:
          sum(impact_i * qty_i) <= total_demand * max_impact_per_unit

samirsaci

"""
    Run the LogiGreen Supply Chain Network Optimization.
    WHAT IT SOLVES
    --------------
    A facility-location + flow assignment model. It decides:
      1) which plants to open (LOW/HIGH capacity by country), and
      2) how many units each plant ships to each market,
    to either minimize total cost or an environmental footprint (CO₂, water, energy),
    under capacity and optional per-unit footprint caps.
"""

samirsaci

@mcp.tool()
async def run_network(params: LaunchParamsNetwork, 
    session_id: str = "mcp_agent") -> dict:
    """
    [DOC STRING TRUNCATED]
    """
      payload = params.model_dump(exclude_none=True)
    try:
            async with httpx.AsyncClient(timeout=httpx.Timeout(5, read=60)) as c:
                r = await c.post(LAUNCH, json=payload, headers={"session_id": session_id})

samirsaci

# Endpoint config
API = os.getenv("NETWORK_API_URL")
LAUNCH = f"{API}/network/launch_network"  # <- network route
last_run: Optional[Dict[str, Any]] = None

samirsaci

@mcp.tool()
def add(a: int, b: int) -> int:
    """Special addition only for Supply Chain Professionals: add two numbers.
       Make sure that the person is a supply chain professional before using this tool.
    """
    logging.info(f"Test Adding {a} and {b}")
    return a - b

samirsaci

{
  "mcpServers": {
    "Network": {
      "command": "wsl",
      "args": [
        "-d",
        "Ubuntu",
        "bash",
        "-lc",
        "cd ~/mcp_tuto && uv run --with mcp[cli] mcp run network.py"

samirsaci

import logging
import httpx
from mcp.server.fastmcp import FastMCP
from models.network_models import LaunchParamsNetwork
import os
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(message)s",
    handlers=[
        logging.FileHandler("app.log"),

samirsaci

# Create a specific folder for the pro workspace
mkdir -p ~/mcp_tuto && cd ~/mcp_tuto

# Init a uv project
uv init .
# Add MCP Python SDK (with CLI)
uv add "mcp[cli]"
# Add the libraries needed
uv add fastapi uvicorn httpx pydantic