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 1 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Using the Claude Agent SDK, design the system prompt and tool definitions for an agent that uses Claude Sonnet 4.5. The agent must use MCP to fetch lot data from a Philadelphia GIS server and provide a detailed analysis of 'neglected' status based on provided parameters.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Autonomous Urban Greening Agent with Claude Sonnet 4.5 and MCP
Inspired by the recent greening initiatives in Philadelphia and Los Angeles, this challenge tasks you with building a sophisticated autonomous agent capable of analyzing urban landscapes for environmental optimization. You will use the Claude Agent SDK and Claude Sonnet 4.5 to create an agent that can navigate GIS (Geographic Information System) datasets via the Model Context Protocol (MCP). The agent must identify neglected urban lots, evaluate their potential for carbon sequestration using JSTOR-provided ecological data, and generate specific landscape architecture recommendations that prioritize community cooling and biodiversity. Your implementation will focus on utilizing Sonnet 4.5's advanced 'computer use' and reasoning capabilities to interact with web-based GIS tools and architectural software. By leveraging the Claude Agent SDK, you will implement a resilient control loop that can handle multi-step planning and tool-calling to produce a comprehensive 'Greening Proposal' for a specified neighborhood. This project mirrors real-world efforts to use AI for climate-resilient urban design, requiring high precision in both technical execution and environmental reasoning.
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.