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, and incompressible Navier--Stokes, MOOSEnger achieves a 0.93 execution pass rate versus 0.08 for an LLM-only baseline.

翻译：MOOSEnger是一款面向多物理场面向对象仿真环境（MOOSE）的工具增强型AI智能体。MOOSE案例通过HIT格式的".i"输入文件进行定义；其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。该智能体采用核心加领域模块的架构设计：可复用的智能体基础设施（配置管理、注册机制、工具调度、检索服务、持久化存储与评估体系）与MOOSE专用插件相分离。插件新增了基于HIT的解析器、保持语法结构的输入文件读取器，以及用于输入修复与检查的领域专用工具集。输入预检流水线首先消除隐藏的格式伪影，通过有限次语法约束循环修复畸变的HIT结构，并借助应用语法注册表的相似性搜索解析无效对象类型。随后，输入文件将通过基于MCP协议的执行后端（支持本地降级方案）进行验证，并可选择在MOOSE运行时环境中进行冒烟测试，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的性能指标（忠实度、相关性、上下文精确率/召回率）以及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、多孔介质流动与不可压缩Navier-Stokes方程的125条提示基准测试中，MOOSEnger实现了0.93的执行通过率，显著优于纯大语言模型基线0.08的通过率。