让大模型自动用剧本数据平台API，靠文档工程提升交互效果
Agentic DraCor and the Art of Docstring Engineering: Evaluating MCP-empowered LLM Usage of the DraCor API
- 用MCP协议让大模型自主调用剧本API工具
- 优化文档编写可显著提升调用准确率与稳定性
- 适合数字人文研究者和AI工具开发者参考
本文实现并评估了为DraCor设计的模型上下文协议（MCP）服务器，使大语言模型（LLM）能自主与DraCor API交互。通过定性实验，系统观察提示词对模型行为的影响，评估了“工具正确性”、“调用效率”和“使用可靠性”。研究揭示“文档工程”的关键作用——即有意识地优化工具说明以提升大模型与工具的协作效果。结果表明，代理型AI在计算文学研究中具有潜力，但也凸显出构建可靠数字人文基础设施的必要性。
原文摘要 · Abstract (English)
This paper reports on the implementation and evaluation of a Model Context Protocol (MCP) server for DraCor, enabling Large Language Models (LLM) to autonomously interact with the DraCor API. We conducted experiments focusing on tool selection and application by the LLM, employing a qualitative approach that includes systematic observation of prompts to understand how LLMs behave when using MCP tools, evaluating "Tool Correctness", "Tool-Calling Efficiency", and "Tool-Use Reliability". Our findings highlight the importance of "Docstring Engineering", defined as reflexively crafting tool documentation to optimize LLM-tool interaction. Our experiments demonstrate both the promise of agentic AI for research in Computational Literary Studies and the essential infrastructure development needs for reliable Digital Humanities infrastructures.
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