Learned User Embeddings from Model Context Protocol Tool Interactions

Model Context Protocol (MCP) is a mechanism that allows Small and Large Language Models to call external data sources in real-time and generate up-to-date and grounded responses for user prompts. MCP servers provide a discrete set of tools which are selected based on reasoning and semantic similarity to the user’s prompt. Due to its discrete nature, use of MCP tools give a unique opportunity to understand user behavior, allowing a Language Model to build personalized experiences such as initial prompt suggestions and customized responses. In this paper, we show that dimension reduction techniques can be applied to build learned representations in the form of fixed size dense embeddings in latent space to represent MCP tool invocations. We apply three different dimensionality reduction techniques on normalized tool invocation counts: Principal Component Analysis (PCA), shared auto-encoder and truncated singular value decomposition. Using a formal approach to the problem we use an experimental data set and show how the learned user embeddings determine similarity between users providing a foundation for personalization related features.