implementation

Integrate RAG and MCP Tools

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Linked challenge: MCP-Enabled Drug Design Agent

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Linked challenge

MCP-Enabled Drug Design Agent

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Original prompt text with formatting preserved for inspection.

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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

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Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

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implementation

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

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.

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