XML, MCP, and Language Models
TL;DR
A “separation of concerns” (between the generative model and the structured data it consults) is proposed as a principled, declarative approach to AI-assisted XML work.
Abstract
This paper reflects on experiments to integrate Small Language Models (SLMs) with XML technologies in academic Digital Humanities work, and discusses the “DigitAI” project. The project is motivated by two goals for making AI systems: 1) more educationally transparent and 2) more economically and environmentally sustainable. We first attempted to make a small local Qwen model serve as an assistant for questions about applying the TEI P5 Guidelines. We provided Qwen with a Retrieval Augmented Generation (RAG) system built from the TEI P5 Guidelines, using XSLT to transform the guidelines into a neo4j knowledge graph. This approach proved both bloated and disappointing: the model's conversion of structured XML data into word embeddings eroded the precision of the original markup, and the outputs of prompts reflected “hallucination” and inaccuracy. Learning from this failure, we came to realize that XML should be kept as XML, held apart from the language model's internal machinery, and made accessible instead through a Model Context Protocol (MCP) server that allows the SLM to query the TEI document tree directly using XPath and related XML stack technologies. We have been experimenting with MCP scripting and evaluating how well SLMs apply XPath, XSLT, and XQuery parsers, giving them agency to evaluate and contribute to XML and TEI XML projects. Much of our learning process has been in preparing a containerized system with Docker for our project to run safely in controlled conditions on personal computers, and to share with others across platforms. Reflecting on our experience, we propose a “separation of concerns” (between the generative model and the structured data it consults) as a principled, declarative approach to AI-assisted XML work.