Free Tool: https://grape-root.vercel.app/ Discord (Debugging/new-updates/feedback) : https://discord.gg/rxgVVgCh If you’ve used Claude Code heavily, you’ve probably seen something like this: “reading file… searching repo… opening another file… following import…” By the time Claude actually understands your system, it has already burned a bunch of tool calls just rediscovering the repo . I started digging into where the tokens were going, and the pattern was pretty clear: most of the cost wasn’t reasoning, it was exploration and re-exploration . So I built a small MCP server called GrapeRoot that gives Claude a better starting context. Instead of discovering files one by one, the model starts with the parts of the repo that are most likely relevant. On the $100 Claude Code plan , that ended up saving about $60/month in my tests. So you can work 3-5x more on 20$ Plan. The interesting failure: I stress tested it with 20 adversarial prompts . Results: 13 cheaper than normal Claude 2 errors 5 more expensive than normal Claude The weird thing: the failures were broad system questions , like: finding mismatches between frontend and backend data mapping events across services auditing logging behaviour Claude technically had context, but not enough of the right context, so it fell back to exploring the repo again with tool calls. That completely wiped out the savings. The realization I expected the system to work best when context was as small as possible . But the opposite turned out to be true. Giving Direction to LLM was actually cheaper than letting the model explore. Rough numbers from the benchmarks: Direction extra Cost ≈ $0.01 extra exploration via tool calls ≈ $0.10–$0.30 So being “too efficient” with context ended up costing 10–30× more downstream . After adjusting the strategy: The strategy included classifying the strategies and those 5 failures flipped. Cost win rate 13 / 18 → 18 / 18 The biggest swing was direction that dropped from $0.882 → $0.345 because the model could understand the system without exploring. Overall benchmark 45 prompts using Claude Sonnet. Results across multiple runs: 40–45% lower cost ~76% faster responses slightly better answer quality Total benchmark cost: $57.51 What GrapeRoot actually does The idea is simple: give the model a memory of the repo so it doesn’t have to rediscover it every turn. It maintains a lightweight map of things like: files functions imports call relationships Then each prompt starts with the most relevant pieces of that map and code. Everything runs locally, so your code never leaves your machine. The main takeaway The biggest improvement didn’t come from a better model. It came from giving the model the right context before it starts thinking. Use this if you too want to extend your usage :) Free tool: https://grape-root.vercel.app/#install submitted by /u/intellinker
Originally posted by u/intellinker on r/ArtificialInteligence