If your AI coding agent keeps reading the same files every time you ask a question, it’s wasting time and tokens on work that could be answered almost instantly.
I’ve been using Claude Code and a few other AI coding agents with a medium-sized Django project for the past few months, and I kept noticing the same problem.
For example, if I asked, “What calls this function?“, the agent would start searching through a large part of the repository. It would consume thousands of tokens, take longer than necessary, and sometimes still miss a function call hidden deep inside the project.
That’s where codebase-memory-mcp helps. Instead of scanning your project from scratch every time, it builds a knowledge graph of your codebase once. After that, the agent can answer structural questions by querying the graph, making responses much faster and more accurate.