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.
Refine the ResearcherAgent to incorporate the LlamaIndex RAG pipeline for information retrieval. Implement the MCP adapter for the SimulationAnalyst agent to interact with the mock scientific API. Test the RAG and MCP functionalities independently.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
MCP-Enabled Drug Design Agent
Develop a cutting-edge multi-agent system designed to accelerate computer-aided drug discovery or battery material design. This challenge focuses on building a sophisticated R&D workflow using graph-based agent orchestration, advanced RAG, and MCP-enabled tool integration. Agents will autonomously research, hypothesize, simulate, and refine designs by interacting with specialized scientific databases and simulation APIs. The system will employ GPT-5 for its advanced reasoning capabilities, leveraging an 'extended thinking' pattern with adaptive reasoning budgets to tackle complex scientific problems. LangGraph will define the sequential and parallel execution of agents, allowing for dynamic re-evaluation and iterative design cycles. LlamaIndex will manage an optimized RAG pipeline over vast scientific literature, ensuring agents have access to the most current and relevant data. Participants will implement MCP for seamless integration with external computational chemistry or materials simulation tools, enabling agents to execute experiments and analyze results within the defined workflow. This project simulates a real-world application of generative AI and agentic systems in scientific discovery, pushing the boundaries of autonomous research.
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.