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.
Develop a mock MCP-enabled tool for secure access to a simulated court case management system. Detail the API endpoints, data schema for legal document retrieval, and how your LangGraph agent will invoke this tool to fetch case-specific information while adhering to security and compliance protocols.
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.
Graph-Based Legal Aid
This challenge tasks you with building a robust, hallucination-resistant legal aid agent system. You will design a graph-based workflow using LangGraph, leveraging Claude Opus 4.5 for nuanced legal reasoning and LlamaIndex for advanced RAG over legal documents. The system must incorporate self-correction mechanisms via A2A protocol for agent verification, and adaptive thinking budgets to ensure accuracy and timely responses in complex probate cases. The core focus is on mitigating factual errors and improving response relevance in sensitive legal contexts. You will integrate MCP-enabled tools for secure access to enterprise legal data and orchestrate the multi-agent system using Letta AI for comprehensive performance monitoring and evaluation, pushing the boundaries of reliable generative AI in public services.
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.