Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 28 active lines to adapt.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
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.
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
MCP Server for Enterprise Sustainability Reporting
Develop a cutting-edge agent system designed for enterprise sustainability reporting. This system will leverage the MCP to securely and interpretably access diverse, simulated internal data sources (e.g., IoT sensor readings from factories, energy consumption logs, supply chain data). Agents, orchestrated in a stateful graph, will analyze this data using Claude Opus 4.5 for high-level reasoning and GPT-4o for tool orchestration, integrating a custom anomaly detection model served by TorchServe. The goal is to generate dynamic, accurate sustainability and compliance reports, identify environmental risks, and provide actionable recommendations for optimization.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.