Umami Analytics MCP Server

Most of these tools pull data directly from the Umami API into Claude Desktop, however get_docs adds in a semantic search step to avoid context window issues with Claude as well as saving on token usage. All of the user journeys for a given event are retrieved using the Umami API and then these are chunked into smaller sections and embedded using an open soruce sentence transformer model from hugging face. Then, based on the question, the most relevant chunks are retrieved and returned to Claude, allowing for analysis of specific actions and behaviours performed by users on the website, something hard to replicate with traditional data visualisation tools. The implementation of this embedding and semantic search is in the src/analytics_service/embeddings.py file.