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.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Develop a mock MCP-enabled tool for accessing a 'Federal Grants Database API'. Integrate this tool into your 'Data Access Agent' such that it can query for grant information based on research topics. Describe how your LangGraph workflow uses the MCP to decide when and how to call this tool.
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 Federal Data Agent
Inspired by the 'Genesis Mission' to boost AI innovation using federal datasets, this challenge tasks you with building a sophisticated agentic system. You will design and implement a graph-based multi-agent system using LangGraph to autonomously access, analyze, and synthesize scientific information from simulated federal datasets. The system will leverage GPT-5 for advanced reasoning and LlamaIndex for robust Retrieval-Augmented Generation (RAG) capabilities, ensuring accurate and contextual information retrieval. A core component will be the integration of MCP-enabled tools for seamless and standardized interaction with various simulated enterprise APIs representing federal data sources, demonstrating how agents can securely and efficiently work with structured and unstructured governmental data for novel scientific insights.
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.