How do you control what context your coding agent sends to an LLM? I built a local tool to measure and audit it — looking for blunt feedback

I’ve been building \*\*mova\*\* — \*context sovereignty before inference\*: you decide what context may reach the AI, and mova leaves evidence of that decision. It’s an open-source Go binary that runs before the LLM call: \Focus (AST) → PII masking → token budget → egress gate (dry-run) → LLM → evidence\ It does not use an LLM to estimate or audit the context, requires no API key for the governance step, and it’s not a gateway or RAG tool. \*\*Why I’m posting this\*\* I kept running into two things when working with coding agents: \* large amounts of context being sent when only a small part of the repository was relevant; \* not having a clear way to see exactly what context was selected, filtered, or blocked before inference. I’m trying to figure out whether this is a problem other developers actually care about, or just something I happen to care about. \*\*A reproducible example\*\* The repository contains fictional data and a Linux amd64 build. \\\bash mova run --count 02-pii-compliance-governance \# 7,153 tokens \\\ In this example: \* Before governance: 20,014 tokens \* After governance: 7,153 tokens (\*\*−64%\*\*) \* AST focus alone: 20,101 → 5,325 tokens \* 171 of 1,694 PII-candidate tokens were pseudonymized \* A smaller fictional repository: 1,965 → 779 tokens (\*\*−60%\*\*) The cost figures shown by the tool are theoretical input-token estimates, not actual API spending. \*\*Limitations\*\* \* The context control applies to context that passes through mova (CLI/chat/MCP/HTTP). \* PII masking is heuristic; I have not measured precision/recall yet. \* mova cannot see context sent directly by an IDE outside its control. \* macOS/Windows/arm64 builds are cross-compiled but not yet validated by me on those target machines. \*\*What I’d really like to know\*\* 1. Is controlling or auditing context a real problem for you when using coding agents? 2. How do you control what your agent sends to an LLM today? 3. Would you deliberately send less context for the same task? What would you filter or check first? 4. Do you care about having evidence of what the model actually received? 5. If you saw a tool like this, would you use it, ignore it, or consider it unnecessary? If you want to see more details, the repository contains the implementation and reproducible examples: github.com/m1guel1982/mova-context Blunt feedback is welcome, including: “This solves a problem I don't have.” That’s actually useful feedback for me.