I built an Code context graph for Agentic Coding

I have been curious about how will having a infrastructure that provides agents the capability to explore code bases as relations, rather than text will change the performance of the AI agents So, for the last few weeks, I have been building a parser that does static analysis of the codebase, creates a graph out of it and makes it available as an MCP, which the agent can explore. I finally got to compare it head to head with Gemma 4 26B and the results have been interesting On giving an open ended problem to explore the request flow path in Apache Kafka, Gemma 4 26B running in Gemini CLI spent 6 minutes reading files, and eventually ran out of rate limits The other agent, similarly powered by Gemma 4 26B only, which had access to the Code graph, ran the exploration in <2 minutes, while being able to generate the whole flow, step by step. I am wondering why context graphs are not becoming more popular and larger workflows still depend on markdown files being fed to agents