MOOSEnger is a tool-enabled AI agent tailored to the Multiphysics Object-Oriented Simulation Environment (MOOSE). MOOSE cases are specified in HIT ".i" input files; the large object catalog and strict syntax make initial setup and debugging slow. MOOSEnger offers a conversational workflow that turns natural-language intent into runnable inputs by combining retrieval-augmented generation over curated docs/examples with deterministic, MOOSE-aware parsing, validation, and execution tools. A core-plus-domain architecture separates reusable agent infrastructure (configuration, registries, tool dispatch, retrieval services, persistence, and evaluation) from a MOOSE plugin that adds HIT-based parsing, syntax-preserving ingestion of input files, and domain-specific utilities for input repair and checking. An input precheck pipeline removes hidden formatting artifacts, fixes malformed HIT structure with a bounded grammar-constrained loop, and resolves invalid object types via similarity search over an application syntax registry. Inputs are then validated and optionally smoke-tested with the MOOSE runtime in the loop via an MCP-backed execution backend (with local fallback), translating solver diagnostics into iterative verify-and-correct updates. Built-in evaluation reports RAG metrics (faithfulness, relevancy, context precision/recall) and end-to-end success by actual execution. On a 125-prompt benchmark spanning diffusion, transient heat conduction, solid mechanics, porous flow, incompressible Navier--Stokes, phase field and plasticity, MOOSEnger achieves a 0.90 execution pass rate versus 0.06 for an LLM-only baseline.

翻译：MOOSEnger是一款专为多物理场面向对象模拟环境（MOOSE）设计的工具增强型AI智能体。MOOSE案例通过HIT格式的".i"输入文件进行配置；庞大的对象目录与严格的语法规范导致初始设置与调试过程耗时较长。MOOSEnger提供对话式工作流，通过结合基于精选文档/示例的检索增强生成技术，以及确定性的、MOOSE感知的解析、验证与执行工具，将自然语言意图转化为可运行的输入文件。其采用核心加领域模块的架构设计，将可复用的智能体基础设施（配置管理、注册系统、工具调度、检索服务、持久化存储与评估机制）与MOOSE专用插件分离。该插件新增基于HIT的解析功能、保持语法结构的输入文件读取能力，以及用于输入修复与检查的领域专用工具集。输入预检流程通过以下步骤实现：首先清除隐藏的格式残留物，随后通过有限次数的语法约束循环修复畸形的HIT结构，最后借助应用语法注册表的相似性搜索解析无效对象类型。输入文件经验证后，可选择通过基于MCP协议的执行后端（支持本地回退机制）在闭环中调用MOOSE运行时进行冒烟测试，并将求解器诊断信息转化为迭代式的验证-修正更新。内置评估系统同时报告检索增强生成指标（忠实度、相关性、上下文精确率/召回率）和通过实际执行判定的端到端成功率。在涵盖扩散、瞬态热传导、固体力学、多孔介质流动、不可压Navier-Stokes方程、相场及塑性力学等领域的125条提示基准测试中，MOOSEnger实现了0.90的执行通过率，而纯大语言模型基线的通过率仅为0.06。