LangGraph State and Node Definition

Define the LangGraph state for your sustainability reporting system, including fields for raw data, processed metrics, compliance status, risks, and recommendations. Then, define the initial nodes for your graph: a 'DataReader' (to interface with MCP), a 'DataAnalyzer' (using Claude Opus 4.1), and a 'RiskAssessor'.

```python
from typing import TypedDict, List, Dict, Any
from langchain_core.messages import BaseMessage
from langgraph.graph import StateGraph, START, END
from langchain_core.tools import tool

# Define the graph state
class AgentState(TypedDict):
    raw_data: List[Dict[str, Any]]
    metrics: Dict[str, float]
    compliance_report: Dict[str, Any]
    risks: List[str]
    recommendations: List[str]
    messages: List[BaseMessage]

# Define nodes (functions)
def data_reader(state: AgentState):
    print("---DATA READER---")
    # Simulate MCP data access
    # In a real scenario, this would involve calling an MCP client with 'mcp_token'
    simulated_raw_data = [
        {"source": "iot_sensors_factoryA", "timestamp": "2024-03-01", "water_usage": 120.5, "energy_usage": 1000},
        {"source": "iot_sensors_factoryA", "timestamp": "2024-03-05", "water_usage": 130.0, "energy_usage": 1100}
        # ... more simulated data
    ]
    return {"raw_data": simulated_raw_data, "messages": [("tool", "Data fetched via MCP simulation.")]}

# Define other nodes like data_analyzer, risk_assessor, report_generator
# and wire them into the graph.
```