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 24 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Develop the 'DataReader' node to simulate MCP interaction, ensuring that data access is contingent on a valid 'mcp_token'. Create a custom LangChain tool for the 'DataAnalyzer' agent that calls a TorchServe endpoint. Deploy a simple dummy model (e.g., a pre-trained `sklearn` model for anomaly detection wrapped in TorchServe) locally or on a cloud service, and configure your LangGraph agent to use this tool.
```python
# MCP-aware data reader (simplified)
def mcp_data_reader_node(state: AgentState):
mcp_token = state.get('data_access_request', {}).get('mcp_token')
if mcp_token != "valid_token_123": # Simplified validation
raise ValueError("Invalid MCP token for data access.")
# Simulate fetching data after valid 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}
]
return {"raw_data": simulated_raw_data, "messages": [("tool", "Data fetched via MCP.")]}
# Example of TorchServe Tool (define your model handler and deploy with TorchServe first)
@tool
def analyze_anomalies_torchserve(data_point: float) -> str:
"""Analyzes a data point for anomalies using a model served by TorchServe."""
# In real scenario, make an HTTP request to your TorchServe endpoint
# response = requests.post("http://localhost:8080/predictions/anomaly_detector", json={"input": data_point})
# return response.json()['prediction']
return "no_anomaly" # Simplified for challenge
# Integrate this tool into your DataAnalyzer agent's capabilities.
# e.g., agent_executor = AgentExecutor(agent=agent, tools=[analyze_anomalies_torchserve])
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
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.