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.
Outline the design for your Model Context Protocol (MCP) layer to integrate with a simulated Map API (e.g., a simple Python function that returns mock location/traffic data). Describe how the agent will formulate requests to this MCP server, how the MCP server processes them, and how it returns structured data to the agent for synthesis. Provide a pseudo-code example of an MCP tool definition within your agent.
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.
Gemini 2.5 Pro Driving Co-Pilot with LangGraph & Hybrid Reasoning
This challenge focuses on building a sophisticated, context-aware driving co-pilot. Developers will design a multi-modal agent system that leverages Gemini 2.5 Pro's advanced conversational capabilities and LangGraph's robust state management to provide real-time, adaptive assistance. The co-pilot will handle complex queries, offer proactive suggestions based on live data, and maintain situational awareness, moving beyond simple turn-by-turn navigation to a truly intelligent driving companion. The core of the system will involve implementing a hybrid instant/deep reasoning architecture. For routine inquiries like 'Where's the nearest gas station?', the agent will use instant reasoning. For complex, multi-step planning or critical safety assessments, it will engage Gemini 2.5 Pro's Deep Think mode, dynamically allocating higher computational resources. Tool integration via a lightweight Model Context Protocol (MCP) will enable seamless access to real-time traffic, weather, and location-based services, making the agent highly practical for real-world applications.
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.