Analytical methods underpin geotechnical engineering practice, yet their implementation remains fragmented across error-prone spreadsheets and opaque proprietary software. While Large Language Models (LLMs) offer transformative potential for streamlining engineering workflows, their statistical nature fundamentally conflicts with the strict determinism required for safety-critical calculations. Their tendency to hallucinate formulas, misinterpret units, or alter methodologies between sessions creates a critical trust gap. This paper introduces GeoMCP, a framework built to bridge this gap via a key insight: engineering methods should be represented as structured data, not embedded code. GeoMCP captures analytical methods as "method cards", declarative JSON files defining formulas, units, applicability limits, and literature citations. A constrained symbolic engine executes these cards with verified dimensional consistency, while structured "Agent Skills" guide LLMs to apply engineering judgment and orchestrate the analysis. By exposing these verified capabilities through the Model Context Protocol (MCP), GeoMCP shifts the role of the AI from an unreliable calculator to an intelligent orchestrator. Validated against an official JRC Eurocode~7 worked example, the framework demonstrates computational parity with traditional approaches while ensuring complete mathematical transparency. Ultimately, GeoMCP provides a blueprint for transitioning the industry from isolated legacy software to an interoperable, AI-ready ecosystem where engineers can leverage modern AI without surrendering professional responsibility.

翻译：分析方法构成了岩土工程实践的基础，但其实现仍分散在易出错的电子表格和不透明的专有软件中。尽管大型语言模型（LLMs）为简化工程工作流程提供了变革性潜力，但其统计本质与安全关键计算所需的严格确定性存在根本冲突。它们倾向于生成虚构公式、误解单位或在不同会话间改变方法，造成了关键的信任鸿沟。本文提出了GeoMCP框架，其核心洞见在于：工程方法应被表示为结构化数据，而非嵌入式代码。GeoMCP将分析方法捕获为"方法卡片"，即定义公式、单位、适用性限值和文献引用的声明式JSON文件。一个受约束的符号引擎以经验证的维度一致性执行这些卡片，而结构化的"智能体技能"则引导LLMs应用工程判断并协调分析流程。通过模型上下文协议（MCP）公开这些已验证的能力，GeoMCP将人工智能的角色从不可靠的计算器转变为智能协调器。基于欧盟规范7（Eurocode~7）官方联合研究中心（JRC）算例的验证表明，该框架在保持与传统方法计算一致性的同时，确保了完全的数学透明度。最终，GeoMCP为行业从孤立的遗留软件向可互操作、支持人工智能的生态系统转型提供了蓝图，使工程师能够在保持专业责任的前提下充分利用现代人工智能技术。